Dependencies
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acl
: clausal modifier of noun
acl
stands for finite and non-finite clauses that modify a nominal. The acl
relation
contrasts with the advcl relation, which is used for adverbial clauses
that modify a predicate. The head of the acl
relation is the noun
that is modified, and the dependent is the head of the clause that
modifies the noun.
muž , o kterém jsem mluvil \n man , about whom I-have talked
acl(muž, mluvil)
acl(man, talked)
This relation is also used for optional depictives. The adjective is taken to modify the nominal of which it provides a secondary predication. See u-dep/xcomp for further discussion of resultatives and depictives.
Alena vstoupila do místnosti smutná . \n Alena entered to room sad .
acl(Alena-1, smutná)
acl(Alena-8, sad)
Namaloval svou ženu nahou . \n He-painted his wife naked .
acl(ženu, nahou)
acl(wife, naked)
Czech also allows finite clausal complements for nouns with
a subset of nouns like fact or report.
These look roughly like relative clauses, but do not have any omitted role in the dependent clause.
These are also analyzed as acl
.
Příčinou není fakt , že by kina navštívilo víc diváků . \n Cause is-not the-fact , that would cinemas visit more filmgoers .
acl(fakt, navštívilo)
acl(the-fact, visit)
advcl
: adverbial clause modifier
An adverbial clause modifier is a clause which modifies a verb or other predicate (adjective, etc.), as a modifier not as a core complement. This includes things such as a temporal clause, consequence, conditional clause, purpose clause, etc. The dependent must be clausal (or else it is an advmod) and the dependent is the main predicate of the clause.
K nehodě došlo , když přicházela noc . \n To accident it-came , when was-falling night .
advcl(došlo, přicházela)
advcl(it-came, was-falling)
Jestliže víš , kdo to udělal , měl bys to říct učiteli . \n If you-know , who it did , should you it tell teacher .
advcl(měl, víš)
advcl(should, you-know)
Spěchal , aby přišel včas . \n He-rushed , in-order-to come in-time .
advcl(Spěchal, přišel)
advcl(He-rushed, come)
advmod
: adverbial modifier
An adverbial modifier of a word is a (non-clausal) adverb or adverbial phrase that serves to modify the meaning of the word.

We differentiate adverbials realized as adverbs (advmod
) and
adverbials realized by noun phrases or adpositional phrases
(obl).
geneticky upravené potraviny \n genetically modified food
advmod(upravené, geneticky)
advmod(modified, genetically)
méně často \n less often
advmod(často, méně)
advmod(often, less)
Kam/ADV chcete/VERB jít/VERB zítra/ADV ?/PUNCT \n Where do-you-want to-go tomorrow ?
advmod(jít, Kam)
advmod(jít, zítra)
advmod(to-go, Where)
advmod(to-go, tomorrow)
advmod:emph
: emphasizing word, intensifier
This is a special class of adverbial modifiers.
It corresponds to the words that are attached in the analytical layer of PDT with the label AuxZ
.
In the tectogrammatical layer they often get the label (functor) RHEM
(rhematizers).
While other adverbial modifiers usually modify verbs, adjectives or adverbs, these emphasizers often modify noun phrases, including prepositional phrases.
zvlášť v pondělí \n especially on Monday
advmod:emph(pondělí, zvlášť)
advmod:emph(Monday, especially)
jen 15 procent \n only 15 percent
advmod:emph(procent, jen)
advmod:emph(percent, only)
Other examples:
- Mohli by obvinit i některého ministra. “They could prosecute also/even a minister.”
- Začnou až o měsíc později. lit. They-will-start even by month later. “They will start one month later.” (Až expresses that the speaker or the listener did not expect the thing to happen that late.)
- Ani vojáci o to nemají zájem. “Not even soldiers are interested in it.”
- Hraje už v sobotu. “He will play already on Saturday.”
- Chceme se sejít ještě tento týden. lit. We-want to meet still this week. “We want to meet before this week ends.”
- u asi 20 titulů “by around/approximately 20 items”
- Dá se to dokumentovat právě na početné skupině dětí. “It can be shown just on a large group of children.”
amod
: adjectival modifier
An adjectival modifier of a noun is any adjectival phrase that serves to modify the meaning of the noun.
Exception: if the modifying adjectival word is pronominal (i.e. tagged cs-pos/DET),
the relation is det instead of amod
.
Václav jí vepřové maso . \n Václav eats pork meat .
amod(maso, vepřové)
amod(meat, pork)
Václav si vzal třímiliónovou půjčku . \n Václav himself took-out three-million loan .
amod(půjčku, třímiliónovou)
amod(loan, three-million)
První závodník byl rychlý . \n First racer was fast .
amod(závodník, První)
amod(racer, First)
nsubj(rychlý, závodník)
nsubj(fast, racer)
Rychlý závodník byl první . \n Fast racer was first .
amod(závodník, Rychlý)
amod(racer, Fast)
nsubj(první, závodník)
nsubj(first, racer)
appos
: appositional modifier
An appositional modifier of a noun is a nominal immediately following the first noun that serves to define or modify that noun. It includes parenthesized examples, as well as defining abbreviations in one of these structures.
Karel IV . , římský císař a český král , vládl ve 14 . století . \n Charles IV . , Roman Emperor and Czech king , ruled in 14 th century .
appos(Karel, císař)
appos(Charles, Emperor)
conj(císař, král)
conj(Emperor, king)
Občanská demokratická strana ( ODS ) \n Civic Democratic Party ( ODS )
appos(strana, ODS-5)
appos(Party, ODS-12)
There is a slight deviation from the universal standard in case of more than one appositive nominal.
Instead of attaching them all to the first noun, all the appositive modifiers are put together in coordination,
then attached as appos
to the modified noun.
This is done regardless whether the appositives are joined by a coordinating conjunction or just a comma.
Přijel Michal , můj bratr a Davidův bratranec . \n Came Michal , my brother and David's cousin .
appos(Michal-2, bratr)
appos(Michal-12, brother)
conj(bratr, bratranec)
conj(brother, cousin)
Michal , můj bratr , Davidův bratranec , přijede zítra . \n Michal , my brother , David's cousin , will-come tomorrow .
appos(Michal-1, bratr)
appos(Michal-13, brother)
conj(bratr, bratranec)
conj(brother, cousin)
appos
is also used to link key-value pairs in addresses, signatures, etc. (see also the list label):
Jan Novák , tel . : 777-000-111 , e-mail<EMAIL_ADDRESS>flat(Jan, Novák)
list(Jan, tel)
list(Jan, e-mail)
appos(tel, 777-000-111)
appos(e-mail<EMAIL_ADDRESS>aux
: auxiliary
An auxiliary of a clause is a form of the auxiliary verb být “to be” used to construct the periphrastic future tense, past tense or conditional.
Exception: Auxiliary verb used to construct the passive
voice is not labeled aux
but aux:pass.
Note that besides aux
and aux:pass
, the verb být may also act as a copula
and as the main verb.
Přijel jsem včera . \n Arrived I-have yesterday .
aux(Přijel, jsem)
aux(Arrived, I-have)
Zítra budu přednášet na univerzitě . \n Tomorrow I-will give-a-talk at university .
aux(přednášet, budu)
aux(give-a-talk, I-will)
Přišel bych dříve , ale ujel mi vlak . \n Come I-would earlier , but missed my train .
aux(Přišel, bych)
aux(Come, I-would)
aux:pass
: passive auxiliary
A passive auxiliary of a clause is a form of the auxiliary verb být “to be” used to construct the periphrastic passive voice (in any tense or in the infinitive).
Kennedy byl zabit . \n Kennedy was killed .
aux:pass(zabit, byl)
aux:pass(killed, was)
Kennedy bude zabit . \n Kennedy will-be killed .
aux:pass(zabit, bude)
aux:pass(killed, will-be)
Kennedy netušil , že jeho osudem je být zabit . \n Kennedy did-not-anticipate that his fate is to-be killed .
aux:pass(zabit, být)
aux:pass(killed, to-be)
Note that the passive participle may be also used as nominal predicate with copula. Hence it may be difficult to distinguish a passive construction from a copula construction. The former focuses on the process while the latter emphasizes the result.
- Passive:
Smlouva byla podepsána v Bílém domě . \n Contract was signed in White House .
aux:pass(podepsána, byla)
aux:pass(signed, was)
- Copula:
Smlouva byla podepsána červeným inkoustem . \n Contract was signed in-red ink .
cop(podepsána, byla)
cop(signed, was)
case
: case marking
Czech uses a combination of oblique morphological cases and prepositions to express semantic case.
Unlike in the original annotation of the Prague Dependency Treebank, prepositions in UD are treated as dependents
of the noun they attach to. The relation between the noun and the preposition is labeled case
.
V pátek.Acc jsme si cestou.Ins do Francie.Gen povídali o té události.Loc . \n On Friday we-have ourselves during-the-journey to France talked about the event .
case(pátek.Acc, V)
case(Friday, On)
case(Francie.Gen, do)
case(France, to)
case(události.Loc, o)
case(event, about)
cc
: coordinating conjunction
For more on coordination, see the conj relation.
A cc
is the relation between a conjunct and
the coordinating conjunction that precedes it.
(Note: different dependency grammars have different treatments of coordination.
We take the first conjunct as the head of the coordination.)
Je to starý a moudrý muž . \n Is he old and wise man .
cc(moudrý, a)
cc(wise, and)
A coordinating conjunction may also appear at the beginning of a
sentence. This is also called a cc
, and it depends on the root
predicate of the sentence.
(In fact there is a coordination that spans multiple sentences.)
A pak jsme odešli . \n And then we-have left .
cc(odešli, A)
cc(left, And)
Máme jablka , hrušky , pomeranče a banány . \n We-have apples , pears , oranges and bananas .
obj(Máme, jablka)
obj(We-have, apples)
conj(jablka, hrušky)
conj(jablka, pomeranče)
conj(jablka, banány)
conj(apples, pears)
conj(apples, oranges)
conj(apples, bananas)
cc(banány, a)
cc(bananas, and)
punct(hrušky, ,-3)
punct(pomeranče, ,-5)
punct(pears, ,-13)
punct(oranges, ,-15)
ccomp
: clausal complement
A clausal complement of a verb or adjective is a dependent clause which is a core argument. That is, it functions like an object of the verb, or adjective.
Říká , že ráda plaveš . \n He-says , that like-to you-swim .
ccomp(Říká, plaveš)
ccomp(He-says, you-swim)
Such clausal complements may be finite or nonfinite. However, if the subject of the clausal complement is controlled (that is, must be the same as the higher subject or object, with no other possible interpretation) the appropriate relation is xcomp.
Šéf nám nařídil vykopat příkop . \n Boss us ordered to-dig ditch .
ccomp(nařídil, vykopat)
ccomp(ordered, to-dig)
Začali jsme kopat příkop . \n Started we-have to-dig ditch .
xcomp(Začali, kopat)
xcomp(Started, to-dig)
The key difference here is that, while it is possible to interpret the first
sentence to mean that the boss will not be doing any digging, in the second
sentence it is clear that the subject of digging can only be we. This is
what distinguishes ccomp
and xcomp
.
compound
: compound
compound
is one of the relations in UD for compounding.
In Czech it is used only for compound numbers.
The rightmost numeral is the head, the other numerals are attached as its modifiers.
Bude to stát nanejvýš 50 tisíc korun . \n Will it cost at-most 50 thousand crowns .
nummod:gov(korun, tisíc)
nummod:gov(crowns, thousand)
compound(tisíc, 50-5)
compound(thousand, 50-14)
Bude to stát padesát pět tisíc korun . \n Will it cost fifty five thousand crowns .
nummod:gov(korun, tisíc)
nummod:gov(crowns, thousand)
compound(tisíc, padesát)
compound(thousand, fifty)
compound(tisíc, pět)
compound(thousand, five)
conj
: conjunct
A conjunct is the relation between two elements connected by a
coordinating conjunction, such as and, or, or a comma or other punctuation.
We treat coordination asymmetrically.
The head of the relation is the first
conjunct and all the other conjuncts depend on it via the conj
relation.
Je to starý a moudrý muž . \n Is he old and wise man .
conj(starý, moudrý)
conj(old, wise)
Máme jablka , hrušky , pomeranče a banány . \n We-have apples , pears , oranges and bananas .
obj(Máme, jablka)
obj(We-have, apples)
conj(jablka, hrušky)
conj(jablka, pomeranče)
conj(jablka, banány)
conj(apples, pears)
conj(apples, oranges)
conj(apples, bananas)
cc(banány, a)
cc(bananas, and)
punct(hrušky, ,-3)
punct(pomeranče, ,-5)
punct(pears, ,-13)
punct(oranges, ,-15)
Coordinate clauses are treated the same way as coordination of other constituent types:
Přišel domů , osprchoval se a hned šel do postele . \n He-came home , showered himself and immediately went to bed .
conj(Přišel, osprchoval)
conj(Přišel, šel)
conj(He-came, showered)
conj(He-came, went)
punct(osprchoval, ,-3)
punct(showered, ,-15)
cc(šel, a)
cc(went, and)
See universal/conj for more details on various coordination-related issues. Note that the present conversion procedure loses some annotations of shared modifiers and nested coordination.
cop
: copula
A copula is the relation between the nominal predicate (přísudek jmenný) and
the copular verb být “to be” (or its variants bývat, bývávat).
The verb stát se “to become”, despite being counted among copular verbs by some authors,
is not analyzed as cop
.
We normally take a copula as a dependent of its complement (the nominal predicate). The nominal predicate is usually a noun, an adjective or a participle.
Ondřej je čestný muž . \n Ondřej is honest man .
cop(muž, je)
cop(man, is)
Ondřej je čestný . \n Ondřej is honest .
cop(čestný, je)
cop(honest, is)
Note that the passive participle may be also used as nominal predicate with copula. Hence it may be difficult to distinguish a passive construction from a copula construction. The former focuses on the process while the latter emphasizes the result.
- Passive: Smlouva byla.
AUX
podepsána v Bílém domě. “The contract was signed in the White House.” - Copula: Smlouva byla.
VERB
podepsána červeným inkoustem. “The contract was signed in red ink.”
Smlouva byla podepsána v Bílém domě . \n Contract was signed in White House .
aux:pass(podepsána, byla)
aux:pass(signed, was)
Smlouva byla podepsána červeným inkoustem . \n Contract was signed in-red ink .
cop(podepsána, byla)
cop(signed, was)
The nominal predicate may also be expressed using a prepositional phrase. Phrases expressing the state of the subject are clear predicates, e.g. Lenka je v kondici “Lenka is in shape”. Under the v2 guidelines, prepositional phrases expressing locations are also considered predicates, to increase parallelism across languages.
Thus the following sentences receive parallel analyses:
- Lenka je skvělá. “Lenka is brilliant.”
- Lenka je v kondici. “Lenka is in shape.”
- Lenka je v kuchyni. “Lenka is in the kitchen.”
In contrast, the following two are no longer parallel:
- Lenka je v kuchyni. “Lenka is in the kitchen.”
- Lenka zůstala v kuchyni. “Lenka stayed in the kitchen.”
 The governor of this relation might not always be a verb: when the verb is a copular verb, the root of the clause is the complement of the copular verb. The dependent is the main lexical verb or other predicate of the subject clause.
Obžalovanému přitížilo , že neměl alibi . \n To-indictee did-a-disservice , that he-did-not-have alibi .
csubj(přitížilo, neměl)
csubj(did-a-disservice, he-did-not-have)
Podstatou těchto vazeb je , že se děj rozloží na dvě složky . \n The-essence of-these constructions is , that one the-action splits to two parts .
csubj(Podstatou, rozloží)
cop(Podstatou, je)
csubj(The-essence, splits)
cop(The-essence, is)
csubj:pass
: clausal passive subject
A clausal passive subject is a clausal syntactic subject of a passive clause.
Bylo mi doporučeno , abych to velmi dobře zvážil . \n It-has-been to-me recommended , that-I it very well weigh .
csubj:pass(doporučeno, zvážil)
csubj:pass(recommended, weigh)
Reflexive passive (the meaning is “You are not expected to come before nine o’clock.”)
Nepředpokládá se , že přijdete před devátou . \n It-does-not-expect itself , that you-will-come before nine .
csubj:pass(Nepředpokládá, přijdete)
csubj:pass(It-does-not-expect, you-will-come)
dep
: unspecified dependency
A dependency is labeled as dep
when a system is unable to
determine a more precise dependency relation between two words. This
may be because of a weird grammatical construction, a limitation in
software, a parser error, or
because of an unresolved long distance dependency.
Most dep
instances in the current conversion of the PDT 3.0 data
correspond to the PDT label ExD
, which marks ellipsis.
The current conversion software cannot produce the annotation fully conforming
to the UD standard and using the orphan relation.
K čemu ovšem , to nebylo jasné . \n For what however , that was-not clear .
dep(jasné, čemu)
dep(clear, what)
det
: determiner
The relation determiner (det
) holds between a nominal head and its
determiner.
This relation is used for pronominal adjectival modifiers of noun phrases; the det
modifier has the POS tag cs-pos/DET and vice versa.
Non-pronominal adjectives are tagged cs-pos/ADJ and the relation is labeled amod.
Pronominal quantifiers are tagged DET
but their relation to their head is a subtype of the det
relation:
either cs-dep/det:numgov or cs-dep/det:nummod.
Ten člověk už je tady . \n The man already is here .
det(člověk, Ten)
det(man, The)
Která kniha se vám líbí nejvíc ? \n Which book is to-you nice the-most ?
det(kniha, Která)
det(book, Which)
det:numgov
: pronominal quantifier governing the case of the noun
Pronominal quantifiers are labeled det:numgov
instead of det
because they normally do not agree with the quantified noun in case
(unlike non-quantifying determiners).
The quantifier requires the counted noun to be in its genitive form. The whole phrase (quantifier + noun) is treated as a singular neuter noun phrase and it can fill roles where nominative, accusative or vocative noun phrases are expected.
Such situations are analyzed in PDT so that the quantifier (numeral) is the head and the noun depends on it.
In UD the dependency direction is reversed and the det:numgov
label is used to preserve
the information about case conditions.
# This is not UD, it is Prague Dependency Treebank, and we want to clearly distinguish it from the UD examples.
# visual-style nodes yellow
# visual-style arcs blue
1 Kolik kolik NUM _ Case=Nom 3 Sb _ How-many
2 mužů muž NOUN _ Case=Gen|Gender=Masc|Number=Plur 1 Atr _ men
3 hrálo hrát VERB _ Gender=Neut|Number=Sing 0 Pred _ played
4 karty karta NOUN _ Case=Acc|Gender=Fem|Number=Plur 3 Obj _ cards
5 ? ? PUNCT _ _ 0 AuxK _ ?
Kolik mužů hrálo karty ? \n How-many men played cards ?
det:numgov(mužů, Kolik)
nsubj(hrálo, mužů)
obj(hrálo, karty)
punct(hrálo, ?-5)
det:numgov(men, How-many)
nsubj(played, men)
obj(played, cards)
punct(played, ?-11)
See nummod for a broader discussion of the various situations with quantifiers.
det:nummod
: pronominal quantifier agreeing in case with the noun
Pronominal quantifiers are labeled det:nummod
or det:numgov instead of det
because they normally do not agree with the quantified noun in case
(unlike non-quantifying determiners).
They do agree only if the whole phrase (quantifier + noun) fills a role where
genitive, dative, locative or instrumental noun phrases are expected.
In these situations they are labeled det:nummod
.
Nepamatuji si , s kolika muži jsem hrál karty . \n I-do-not-remember myself , with how-many men I-have played cards .
ccomp(Nepamatuji, hrál)
expl:pv(Nepamatuji, si)
punct(hrál, ,-3)
aux(hrál, jsem)
obj(hrál, karty)
iobj(hrál, muži)
case(muži, s)
det:nummod(muži, kolika)
punct(Nepamatuji, .-10)
ccomp(I-do-not-remember, played)
expl:pv(I-do-not-remember, myself)
punct(played, ,-14)
aux(played, I-have)
obj(played, cards)
iobj(played, men)
case(men, with)
det:nummod(men, how-many)
punct(I-do-not-remember, .-21)
See nummod for a broader discussion of the various situations with quantifiers.
discourse
: discourse element
At present we use the discourse
relation for what is labeled AuxO
in the Prague Dependency Treebank:
redundant or emotional items, redundant coreferential pronoun.
čemu že se to zpronevěřily \n what that themselves it they-betrayed
discourse(zpronevěřily, to)
discourse(they-betrayed, it)
(meaning “… what they betrayed”)
expl:pass
: reflexive pronoun used in reflexive passive
Reflexive pronouns (see the feature cs-feat/Reflex) are used in various constructions in Czech,
including so-called reflexive passive.
In PDT, their relation to the verb is labeled AuxR
.
The corresponding label in Czech UD is called expl:pass
(since UD 2.0; in previous versions it was labeled auxpass:reflex
).
To se řekne snadno . \n It is said easily .
expl:pass(řekne, se)
expl:pass(said, is)
expl:pv
: reflexive clitic with an inherently reflexive verb
Reflexive pronouns (see the feature cs-feat/Reflex) usually replace objects of verbs. However, some verbs are inherently reflexive, i.e. the verb always occurs with a reflexive prounoun, and the pronoun cannot be replaced by a non-reflexive pronoun.
With these verbs, the reflexive pronoun is attached as expl:pv
instead of obj.
(Note that the expl
relation is first used for this purpose in the UD release 1.2,
and it is further subtyped as expl:pv
since UD 2.0,
to increase parallelism with other languages. In the previous releases this usage of
reflexive se/si was labeled compound:reflex
.)
Martin se bojí zvířat . \n Martin REFLEX fears animals .
expl:pv(bojí, se)
expl:pv(fears, REFLEX)
fixed
: multi-word expression
The fixed
multi-word expression relation is one of the
relations for compounding and it is intended mostly for function words.
In Czech it is used for multi-word prepositions.
The leftmost word is the head and all the other words are attached as its fixed
modifiers.
Na rozdíl od tebe já se nemám zač stydět . \n In contrast to you I myself have-not of-what to-be-ashamed .
fixed(Na, rozdíl)
fixed(Na, od)
case(tebe, Na)
fixed(In, contrast)
fixed(In, to)
case(you, In)
Multi-word prepositions usually form a contiguous span of the sentence. In exceptional cases however, they can be interrupted by emphasizers and other words:
ve srovnání například s úvěry \n in comparison for-example to loans
fixed(ve, srovnání)
fixed(ve, s)
case(úvěry, ve)
advmod:emph(úvěry, například)
fixed(in, comparison)
fixed(in, to)
case(loans, in)
advmod:emph(loans, for-example)
Another type of interruption is caused by a possessive determiner. For instance, na základě něčeho “based on something” contains the multi-word preposition na základě “based on”, lit. on the-basis-of. This expression is etymologically a combination of a proper preposition na “on” and the noun základ “basis”; the argument něčeho “something” is in genitive. If the phrase na základě were not analyzed as a multi-word expression, we would have a normal genitive modification between two nominals: základ něčeho “basis of-something”. A genitive modifier can be substituted by a possessive determiner, and this is possible even within the partially frozen multi-word preposition, as with the relative possessive determiner in the following example:
zákon , na jehož základě byl zřízen fond \n law , on whose basis was created fund
nmod(zřízen, jehož)
nmod(created, whose)
case(jehož, na)
case(whose, on)
fixed(na, základě)
fixed(on, basis)
flat
: personal name
flat
is one of the relations for compounding in UD.
In Czech it is used to join the first (or middle) and the last name of a person.
The leftmost name is always the head and the other name(s) are attached to it. In most cases this is the opposite to PDT where family names are heads and given names are dependents, and the family name is usually the last name.
The relation is not used to attach adjectives to nouns within multi-word names of places, organizations etc. (e.g. Český Krumlov).
Prezident Václav Havel včera navštívil Český Krumlov . \n President Václav Havel yesterday visited Český Krumlov .
flat(Václav-2, Havel-3)
flat(Václav-11, Havel-12)
nmod(Václav-2, Prezident)
nmod(Václav-11, President)
amod(Krumlov-7, Český-6)
amod(Krumlov-16, Český-15)
flat:foreign
: foreign words
We use flat:foreign
to label sequences of foreign words. These are given
a linear analysis: the head is the first token in the foreign phrase.
flat:foreign
does not apply to loanwords or to foreign names.
It applies to quoted foreign text incorporated in a sentence/discourse
of the host language (unless we want to and know how to annotate the
internal structure according to the syntax of the foreign language).
Jarmusch se objevil ve Wangově snímku Modrá ve tváři ( Blue in the Face ) .
flat:foreign(Blue, in)
flat:foreign(Blue, the)
flat:foreign(Blue, Face)
goeswith
: goes with
This relation links two parts of a word that are separated in text
that is not well edited.
The head is always the first part, the other parts are attached to it with the goeswith
relation
(for consistency, similarly as in flat, fixed and conj).
Note that only the last part may be annotated with SpaceAfter=No
.
Zastavil a z těžka oddychoval .
goeswith(z, těžka)
iobj
: indirect object
The indirect object of a verb is any nominal phrase that is a core argument of the verb but is not its subject or direct object. The prototypical example is the recipient of ditransitive verbs of exchange:
Pavel dal Petrovi dvě hrušky . \n Pavel gave Petr two pears .
iobj(dal, Petrovi)
iobj(gave, Petr)
However, Czech allows other semantic roles as additional objects. The morphological cs-feat/Case of the objects is dictated by verb valency.
In the following Czech example, the verb takes two arguments, both are nouns in the accusative case. One of them is direct object (patient), the other is indirect (addressee). It is parallel to how the English translation would be annotated (where there is no morphological case marking) and also to verbs of giving (consider a similar sentence, he gave my daughter a class of maths).
On učí mou dceru matematiku . \n He teaches my daughter.Acc maths.Acc .
obj(učí, matematiku)
iobj(učí, dceru)
obj(teaches, maths.Acc)
iobj(teaches, daughter.Acc)
In general, if there is just one object, it should be labeled obj, regardless of the morphological case or semantic role. For example, učit “to teach” can take either the subject matter or the recipient as the only object, and in both cases it would be analyzed as the obj:
Učí úvod do logiky . \n He-teaches introduction to logic .
obj(Učí, úvod)
obj(He-teaches, introduction)
Učí studenty prvního ročníku . \n He-teaches students of-first year .
obj(Učí, studenty)
obj(He-teaches, students)
The one exception is when there is a clausal complement.
Then the clausal complement is regarded as a “clausal direct object” and an object nominal will be an iobj
, parallel to
the simple ditransitive case:
Řekla studentům , že dnes večer musí studovat . \n She-told students , that today evening they-needed to-study .
iobj(Řekla, studentům)
ccomp(Řekla, musí)
iobj(She-told, students)
ccomp(She-told, they-needed)
Řekla studentům svůj plán . \n She-told students her plan .
iobj(Řekla, studentům)
obj(Řekla, plán)
iobj(She-told, students)
obj(She-told, plan)
If there are two or
more objects, one of them should be obj and the others should be
iobj
. In such cases it is necessary to decide what is the
most directly affected object (patient).
Diffs
Prague Dependency Treebank
The manual annotation of the PDT does not distinguish direct and indirect objects.
Therefore most non-clausal dependents labeled Obj
in PDT are currently labeled obj
in the converted data, even if it results in two or more direct objects attached to one verb.
Occasionally a heuristic was used: if there are two objects, one of them accusative and the other dative, then the former is obj and the latter iobj.
But such heuristics do not cover all possible objects, and even ditransitive verbs may occur without one of their objects because of ellipsis.
In future, the valency lexicon Vallex
could be used to identify the main object.
list
: list
The list
relation is used for chains of comparable items.
In lists with more than two items, all items of the list should modify the first one.
Informal and web text often contains passages which are meant to be interpreted as lists but are parsed as single sentences.
Email signatures often contain these structures, in the form of contact information:
the different contact information items are labeled as list
; the key-value pair relations are labeled as appos.
However, list
should not be over-used. If a construction can be easily analyzed
using the grammatical relations of standard sentences, such as when there is overt coordination,
then it should be analyzed with these more standard relations, even if it is laid out as a list typographically.
Steve Jones tel.: 555-9814 e-mail<EMAIL_ADDRESS>flat(Steve-1, Jones-2)
list(Steve-1, tel.:-3)
list(Steve-1, e-mail:-5)
appos(tel.:-3, 555-9814-4)
appos(e-mail:-5<EMAIL_ADDRESS>Diffs
Prague Dependency Treebank
The list
relation is not used in the current conversion of the PDT data to UD.
mark
: marker
A marker is the subordinating conjunction introducing a finite clause subordinate to another clause. The mark is a dependent of the subordinate clause head.
Nevěděl jsem , že umíš německy . \n Not-know I-did , that you-can-speak German .
mark(umíš, že)
mark(you-can-speak, that)
Zboží odešleme , jakmile peníze dorazí na náš účet . \n Goods we-will-dispatch , as-soon-as money arrive at our account .
mark(dorazí, jakmile)
mark(arrive, as-soon-as)
Note that the subordinating conjunction should not be confused with relative pronouns and adverbs.
These fill a valency slot in the frame of the subordinate predicate,
and are labeled according to their role in the frame, they are not mark
:
Rád bych věděl , kdy peníze dorazí na náš účet . \n Like-to I-would know , when money arrive at our account .
advmod(dorazí, kdy)
advmod(arrive, when)
neg
: negation modifier
The negation modifier is the relation between the negation word ne and the word it modifies.
Negation in Czech is most of the time expressed using a bound morpheme (the prefix ne-). Occurrences of the morpheme as a separate word are rare in comparison to other languages, yet they exist.
Potřeboval čtyřnohého a ne dvounohého přítele . \n He-needed quadrupedal and not bipedal friend .
neg(dvounohého, ne)
neg(bipedal, not)
Note that the negative determiners (pronouns) are attached as det, not neg
:
Premiér není žádný hlupák . \n The-Prime-Minister is-not no fool .
det(hlupák, žádný)
det(fool, no)
nmod
: nominal modifier
The nmod
relation is used for nominal modifiers of nominals.
It usually corresponds to a non-agreeing attribute (přívlastek neshodný) in genitive. (See below for an example of an agreeing attribute, přívlastek shodný.)
In Czech the nmod
relation covers only those possessives that are expressed using the genitive cs-feat/Case.
If a possessive adjective is used, the relation is labeled amod.
kancelář ředitele \n office of-the-director
nmod(kancelář, ředitele)
nmod(office, of-the-director)
ředitelova kancelář \n director's office
amod(kancelář, ředitelova)
amod(office, director's)
jeho kancelář \n his office
det(kancelář, jeho)
det(office, his)
Agreeing attribute of a noun
nmod
noun phrases attached to nouns are usually in the genitive cs-feat/Case and follow the modified noun.
However, there is also a different kind of nmod
that precedes the modified noun and agrees with it in case and number.
A typical example is a title attached to a name of a person. The relation is similar to the flat relation
that links the first and the last name, but it is not labeled flat
because the title is not part of the name:
český prezident Václav Havel
amod(prezident, český)
nmod(Václav, prezident)
flat(Václav, Havel)
Note that the same thing can be also expressed using an apposition. In the case of apposition, the title follows the modified name and is separated by a punctuation symbol:
Václav Havel , český prezident
flat(Václav, Havel)
punct(prezident, ,-3)
amod(prezident, český)
appos(Václav, prezident)
nsubj
: nominal subject
A nominal subject is a nominal phrase which is the syntactic subject of a clause;
in Czech, the phrase is in the nominative cs-feat/Case.
(See csubj for when the subject is clausal.
See nsubj:pass and csubj:pass for when the subject is not the proto-agent argument due to valence changing operations.)
The governor of the nsubj
relation might not always be a verb: when
the verb is a copular verb, the root of the clause is the complement
of the copular verb, which can be an adjective or noun.
Novosvětskou symfonii napsal Antonín Dvořák . \n From-the-New-World Symphony wrote Antonín Dvořák .
nsubj(napsal, Dvořák-5)
nsubj(wrote, Dvořák-12)
Auto je červené . \n Car is red .
nsubj(červené, Auto)
nsubj(red, Car)
nsubj:pass
: passive nominal subject
A passive nominal subject is a noun phrase which is the syntactic subject of a passive clause.
Schwarzenberg byl poražen Zemanem . \n Schwarzenberg was defeated by-Zeman .
nsubj:pass(poražen, Schwarzenberg-1)
nsubj:pass(defeated, Schwarzenberg-7)
Reflexive passive (the meaning is “This will be solved tomorrow.”)
Tohle se bude řešit zítra . \n This itself will solve tomorrow .
nsubj:pass(řešit, Tohle)
nsubj:pass(solve, This)
nummod
: numeric modifier
A numeric modifier of a noun is any number phrase that serves to modify the meaning of the noun with a quantity.
Jan snědl tři řízky . \n Jan ate three steaks .
nummod(řízky, tři)
nummod(steaks, three)
Agreement and government with Czech quantifiers
The morphological and syntactic behavior of Czech numerals is a complex matter. Small cardinal numerals jeden “one”, dva “two”, tři “three” and čtyři “four” agree with the counted noun in cs-feat/Case (jeden also agrees in cs-feat/Gender and cs-feat/Number; dva also agrees in cs-feat/Gender). They behave as if they modify the counted noun; they are similar to adjectives in this respect. Examples:
- Jeden muž spal, dva muži hráli karty. “One man slept, two men played cards.”
- Jedna žena spala, dvě ženy hrály karty. “One woman slept, two women played cards.”
- Jedno kotě spalo, dvě koťata si hrála. “One kitten slept, two kittens played.”
In PDT, these numerals are attached to their counted nouns as Atr
(attribute).
It is straightforward to convert such dependencies to nummod
:
Jedno kotě spalo . \n One kitten slept .
nummod(kotě, Jedno)
nsubj(spalo, kotě)
punct(spalo, .-4)
nummod(kitten, One)
nsubj(slept, kitten)
punct(slept, .-9)
Larger cardinals behave differently. They require that the counted noun be in the genitive case; this indicates that they actually govern the noun. Such constructions are parallel to nouns modified by other noun phrases in genitive. The whole phrase (numeral + counted noun) behaves as a noun phrase in neuter gender and singular number (which is important for subject-verb agreement).
- Pět mužů hrálo karty. “Five men played cards.”
- Skupina mužů hrála karty. “A group of men played cards.”
In PDT, these numerals are analyzed as heads of the counted nouns, which are attached to the numeral as Atr
:
# This is not UD, it is Prague Dependency Treebank, and we want to clearly distinguish it from the UD examples.
# visual-style nodes yellow
# visual-style arcs blue
1 Pět pět NUM _ Case=Nom 3 Sb _ Five
2 mužů muž NOUN _ Case=Gen|Gender=Masc|Number=Plur 1 Atr _ men
3 hrálo hrát VERB _ Gender=Neut|Number=Sing 0 Pred _ played
4 karty karta NOUN _ Case=Acc|Gender=Fem|Number=Plur 3 Obj _ cards
5 . . PUNCT _ _ 0 AuxK _ .
There are both advantages and drawbacks to this solution. On the one hand, it reflects well the agreement in case, gender and number. On the other hand, it is confusing that there are two different analyses of counted noun constructions, depending on the numeric value.
Moreover, the numeral does not govern the noun in all morphological cases. The following table shows the case of the whole phrase (numeral + noun; first column) and the consequences for the case of the parts (note that these numerals have only two distinct morphological forms, resulting in homonymy).
| Phrase Case | Example | Numeral Case | Noun Case |
|---|---|---|---|
| Nom | pět mužů | Nom | Gen |
| Gen | pěti mužů | Gen | Gen |
| Dat | pěti mužům | Dat | Dat |
| Acc | pět mužů | Acc | Gen |
| Voc | pět mužů | Voc | Gen |
| Loc | pěti mužích | Loc | Loc |
| Ins | pěti muži | Ins | Ins |
We can say that the noun has the case of the whole phrase if it is dative, locative or instrumental. The numeral then agrees with the noun in case. The numeral forces the noun to the genitive case if the whole phrase is nominative, accusative or vocative (but the vocative usage is rather hypothetical). In genitive, the noun and the numeral agree with each other; but note that the numeral uses its inflected form, as in the other cases where it agrees with the noun.
 PUNCT _ _ 0 AuxK _ .
High-value numerals where the lowest-order digit is more than zero and less than five (e.g. 21, 22, 23, 24) may behave both ways:
- dvacet dva muži (noun governs numeral)
- dvacet dva mužů (numeral governs noun)
- dvaadvacet mužů (alternative form; it does not end with dva, thus the numeral governs the noun)
- 22 muži (assuming the reader will pronounce 22 as dvacet dva, not dvaadvacet)
- 22 mužů (pronounced either way)
Pronominal quantifiers behave as high-value numerals and govern the quantifed nouns:
- Kolik mužů hrálo karty? “How many men played cards?”
- Několik (mnoho, málo) mužů hrálo karty. “Several (many, few) men played cards.”
- Tolik mužů hrát karty jsem ještě neviděl. “I have never seen so many men playing cards.”
# This is not UD, it is Prague Dependency Treebank, and we want to clearly distinguish it from the UD examples.
# visual-style nodes yellow
# visual-style arcs blue
1 Kolik kolik NUM _ Case=Nom 3 Sb _ How-many
2 mužů muž NOUN _ Case=Gen|Gender=Masc|Number=Plur 1 Atr _ men
3 hrálo hrát VERB _ Gender=Neut|Number=Sing 0 Pred _ played
4 karty karta NOUN _ Case=Acc|Gender=Fem|Number=Plur 3 Obj _ cards
5 ? ? PUNCT _ _ 0 AuxK _ ?
The UD conversion of the PDT data unifies analyses of counted noun phrases and uses a structure that is parallel among all the above cases, and also with universal dependencies in other languages. The counted noun is always the head and the numeral is always attached as its modifier. Nevertheless, we use different relation labels to mark situations where the numeral (or quantifier) actually governs the morphological case of the noun. There are four labels used:
| Numeric | Pronominal | |
| Noun governs | nummod | det:nummod |
| Numeral governs | nummod:gov | det:numgov |
Tři muži hráli karty . \n Three men played cards .
nummod(muži, Tři)
nsubj(hráli, muži)
obj(hráli, karty)
punct(hráli, .-5)
nummod(men, Three)
nsubj(played, men)
obj(played, cards)
punct(played, .-11)
Pět mužů hrálo karty . \n Five men played cards .
nummod:gov(mužů, Pět)
nsubj(hrálo, mužů)
obj(hrálo, karty)
punct(hrálo, .-5)
nummod:gov(men, Five)
nsubj(played, men)
obj(played, cards)
punct(played, .-11)
Kolik mužů hrálo karty ? \n How-many men played cards ?
det:numgov(mužů, Kolik)
nsubj(hrálo, mužů)
obj(hrálo, karty)
punct(hrálo, ?-5)
det:numgov(men, How-many)
nsubj(played, men)
obj(played, cards)
punct(played, ?-11)
Hrál jsem karty s pěti muži . \n Played I-have cards with five men .
aux(Hrál, jsem)
obj(Hrál, karty)
iobj(Hrál, muži)
case(muži, s)
nummod(muži, pěti)
punct(Hrál, .-7)
aux(Played, I-have)
obj(Played, cards)
iobj(Played, men)
case(men, with)
nummod(men, five)
punct(Played, .-15)
Nepamatuji si , s kolika muži jsem hrál karty . \n I-do-not-remember myself , with how-many men I-have played cards .
ccomp(Nepamatuji, hrál)
expl:pv(Nepamatuji, si)
punct(hrál, ,-3)
aux(hrál, jsem)
obj(hrál, karty)
iobj(hrál, muži)
case(muži, s)
det:nummod(muži, kolika)
punct(Nepamatuji, .-10)
ccomp(I-do-not-remember, played)
expl:pv(I-do-not-remember, myself)
punct(played, ,-14)
aux(played, I-have)
obj(played, cards)
iobj(played, men)
case(men, with)
det:nummod(men, how-many)
punct(I-do-not-remember, .-21)
Additional remarks
In PDT the words milión “million”, miliarda “billion” and higher are usually tagged as nouns, not as numerals. In the typical case, the million is in genitive, it is preceded by a smaller number, and it is not followed by smaller numerals (as it is in million five hundred thousand). It is followed by the counted noun. Thus the following examples receive parallel analyses:
50 miliónů korun \n 50 millions of-crowns
nummod:gov(miliónů, 50-1)
nummod:gov(millions, 50-5)
nmod(miliónů, korun)
nmod(millions, of-crowns)
50 pytlů bankovek \n 50 sacks of-bills
nummod:gov(pytlů, 50-1)
nummod:gov(sacks, 50-5)
nmod(pytlů, bankovek)
nmod(sacks, of-bills)
On the other hand the word tisíc “thousand” may be a noun (na náměstí byly tisíce lidí “there were thousands of people in the square”) or a numeral:
nanejvýš 50 tisíc korun \n at-most 50 thousand crowns
advmod:emph(korun, nanejvýš)
nummod:gov(korun, tisíc)
compound(tisíc, 50-2)
advmod:emph(crowns, at-most)
nummod:gov(crowns, thousand)
compound(thousand, 50-7)
Note that the two numeral words in the above example are joined using the compound relation. Also note that the intensifier nanejvýš is attached to the head of the phrase (korun) and not to the number. This is in accord both with the UD guidelines and with the original PDT annotation of agreeing numerals (e.g. jen čtyři firmy, jen několik procent).
 PUNCT _ _ 7 AuxG _ th
9 12 12 NUM _ _ 6 Adv _ December
10 . .
Ředitel navrhl zrušit profesionální scénu k 31 . 12 .    PUNCT _ _ 0 AuxK _ .
Letošní veletrh se uskuteční od 9 . do 12 . března . \n This-year's fair itself will-take-place from 9 th to 12 th March .
advmod(uskuteční, března)
case(března, do)
nummod(března, 12-9)
remnant(12-9, 9-6)
remnant(do, od)
advmod(will-take-place, March)
case(March, to)
nummod(March, 12-22)
remnant(12-22, 9-19)
remnant(to, from)
Numbered objects
House number in address is attached as nummod
to the name of the street:
v budově Na poříčí 12 \n in the-building Na poříčí 12
nmod(budově, poříčí-4)
case(poříčí-4, Na-3)
nummod(poříčí-4, 12-5)
nmod(the-building, poříčí-10)
case(poříčí-10, Na-9)
nummod(poříčí-10, 12-11)
nummod:gov
: numeric modifier governing the case of the noun
nummod:gov
differs from nummod
in that the numeral requires the counted noun to be in its genitive form.
The whole phrase (numeral + noun) is treated as a singular neuter noun phrase
and it can fill roles where nominative, accusative or vocative noun phrases are expected.
Such situations are analyzed in PDT so that the numeral is the head and the noun depends on it.
In UD the dependency direction is reversed and the nummod:gov
label is used to preserve
the information about case conditions.
 PUNCT _ _ 0 AuxK _ .
Pět mužů hrálo karty . \n Five men played cards .
nummod:gov(mužů, Pět)
nsubj(hrálo, mužů)
obj(hrálo, karty)
punct(hrálo, .-5)
nummod:gov(men, Five)
nsubj(played, men)
obj(played, cards)
punct(played, .-11)
See nummod for a broader discussion of the various situations with quantifiers.
obj
: direct object
The direct object of a verb is the noun phrase that denotes the entity acted upon. Most often the direct object is in the accusative case but there are verbs that require their objects be in other cases (except for nominative and vocative).
Accusative example:
Koupil jsem auto . \n Bought I-have car .
obj(Koupil, auto)
obj(Bought, car)
Genitive example:
Cením si vaší pomoci . \n I-appreciate REFLEX your help .
obj(Cením, pomoci)
obj(I-appreciate, help)
Dative example:
Čelíme velkým problémům . \n We-face big problems .
obj(Čelíme, problémům)
obj(We-face, problems)
Instrumental example:
Univerzita nedisponuje takovým rozpočtem . \n University does-not-have-at-disposal such budget .
obj(nedisponuje, rozpočtem)
obj(does-not-have-at-disposal, budget)
In general, if there is just one object, it should be labeled obj
,
regardless of the morphological case or semantic role that it bears. If there are two or more
objects, one of them should be obj
and the others should be
iobj. In such cases it is necessary to decide what is the most
directly affected object (patient). The one exception is when there is a clausal complement. Then the clausal complement is regarded as a “clausal direct object” and an object nominal will be an iobj.
Diffs
Prague Dependency Treebank
The manual annotation of the PDT does not distinguish direct and indirect objects.
Therefore most non-clausal dependents labeled Obj
in PDT are currently labeled obj
in the converted data, even if it results in two or more direct objects attached to one verb.
Occasionally a heuristic was used: if there are two objects, one of them accusative and the other dative,
then the former is obj
and the latter iobj
. But such heuristics do not cover all possible objects,
and even ditransitive verbs may occur without one of their objects because of ellipsis.
In future, the valency lexicon Vallex
could be used to identify the main object.
obl
: oblique argument or adjunct
The obl
relation is used for oblique nominal arguments and adjuncts of verbs, adjectives or adverbs.
obl
is a noun (or noun phrase) functioning as a
non-core (oblique) argument or adjunct.
This means that it functionally corresponds to an adverbial when it attaches to a verb, adjective or other adverb.
Potkal jsem ho minulý čtvrtek . \n Met I-have him last Thursday .
obl(Potkal, čtvrtek)
obl(Met, Thursday)
orphan
: connecting orphaned arguments of elided predicate
The orphan
relation is used to provide a satisfactory treatment of certain instances of
ellipsis
(in the case of gapping and stripping, where a predicational or verbal
head gets elided).
Pavel si objednal hovězí a Markéta vepřové . \n Pavel himself ordered beef and Markéta pork .
nsubj(objednal, Pavel-1)
nsubj(ordered, Pavel-10)
obj(objednal, hovězí)
obj(ordered, beef)
conj(objednal, Markéta-6)
conj(ordered, Markéta-15)
orphan(Markéta-6, vepřové)
orphan(Markéta-15, pork)
cc(Markéta-6, a)
cc(Markéta-15, and)
parataxis
: parataxis
The parataxis relation (from Greek for “place side by side”) is a relation between a word (often the main predicate of a sentence) and other elements, such as a sentential parenthetical or a clause after a “:” or a “;”, placed side by side without any explicit coordination, subordination, or argument relation with the head word. Parataxis is a discourse-like equivalent of coordination, and so usually obeys an iconic ordering. Hence it is normal for the first part of a sentence to be the head and the second part to be the parataxis dependent, regardless of the headedness properties of the language. But things do get more complicated, such as cases of parentheticals, which appear medially.
An inventory of constructions to which parataxis has been applied
Treatment of reported speech
For this reported speech example:
Ten člověk , řekl Honza , odjel brzy ráno . \n The guy , said Honza , left early in-the-morning .
parataxis(odjel, řekl)
parataxis(left, said)
there are paraphrases that convey essentially the same meaning but with a different syntactic structure. When the reported speech is embedded in a subordinate clause (with or without an overt complementizer že “that”), the subordinate clause is a ccomp of the speech verb. When the reported speech follows the speech verb and is separated by a colon, the reported speech forms a main clause that attaches to the preceding main clause with a parataxis relation, hence with the speech verb as its head. However, when the speech verb occurs as a medial or final parenthetical, the relation is reversed and the speech verb is treated as a parataxis of the reported speech. This analysis is not uncontroversial but follows many authorities, such as Huddleston and Pullum (2002), The Cambridge Grammar of the English Language (see chapter 11, section 9).
Honza řekl , že ten člověk odjel brzy ráno . \n Honza said , that the guy left early in-the-morning .
ccomp(řekl, odjel)
ccomp(said, left)
Honza řekl : „ Ten člověk odjel brzy ráno . “ \n Honza said : “ The guy left early in-the-morning . ”
parataxis(řekl, odjel)
parataxis(said, left)
„ Ten člověk odjel brzy ráno , “ řekl Honza . \n “ The guy left early in-the-morning , ” said Honza .
parataxis(odjel, řekl)
parataxis(left, said)
„ Ten člověk , “ řekl Honza , „ odjel brzy ráno . “ \n “ The guy , ” said Honza , “ left early in-the-morning . ”
parataxis(odjel, řekl)
parataxis(left, said)
An argument for this analysis is that in the cases analyzed as embedding, the entire clause can be further embedded (I was taken aback when John said the guy left early in the morning.), while this is not possible with medial or final placement of the speech verb (*I was taken aback when the guy left early this morning, John said.).
News article bylines
The parataxis
relation should be used to connect the parts of a news article byline.
There does not seem to be a better relation to use.
Washington ( CNN ) :
parataxis(Washington, CNN)
Interjected clauses and phrases
V posledních letech se srovnávání rozšiřuje , přinejmenším pokud jde o platy , na sousední státy . \n In recent years is comparison extended , at-least as concerns about salaries , to neighboring countries .
parataxis(rozšiřuje, jde)
parataxis(extended, concerns)
Diffs
Prague Dependency Treebank
At present the PDT data converted to UD use parataxis
only for interjected parentheticals.
The other examples above are analyzed differently (legacy PDT structure).
punct
: punctuation
This is used for any piece of punctuation in a clause. See
Jdi domů ! \n Go home !
punct(Jdi, !-3)
punct(Go, !-7)
Tokens with the relation punct
always attach to content words (except in cases of ellipsis) and can never have dependents.
Since punct
is not a normal dependency relation, the usual criteria for determining the head word do not apply.
Instead, we use the following principles:
- A punctuation mark separating coordinated units is attached to the following conjunct.
- A punctuation mark preceding or following a subordinated unit is attached to this unit.
- Within the relevant unit, a punctuation mark is attached at the highest possible node that preserves projectivity.
- Paired punctuation marks (quotes and brackets) should be attached to the same word unless that would create non-projectivity. This word is usually the head of the phrase enclosed in the paired punctuation.
Máme jablka , hrušky , pomeranče a banány . \n We-have apples , pears , oranges and bananas .
obj(Máme, jablka)
obj(We-have, apples)
conj(jablka, hrušky)
conj(jablka, pomeranče)
conj(jablka, banány)
conj(apples, pears)
conj(apples, oranges)
conj(apples, bananas)
cc(banány, a)
cc(bananas, and)
punct(hrušky, ,-3)
punct(pomeranče, ,-5)
punct(pears, ,-13)
punct(oranges, ,-15)
Muž , kterého jste včera viděl , přišel zas . \n Man , whom you-have yesterday seen , came again .
punct(viděl, ,-2)
punct(seen, ,-13)
punct(viděl, ,-7)
punct(seen, ,-18)
punct(přišel, .-10)
punct(came, .-21)
Zkratka např . znamená „ například “ . \n The-abbreviation eg . means “ for-example ” .
punct(např, .-3)
punct(eg, .-12)
punct(například, „-5)
punct(for-example, “-14)
punct(například, “-7)
punct(for-example, ”-16)
punct(znamená, .-8)
punct(means, .-17)
reparandum
: overridden disfluency
We use reparandum
to indicate disfluencies overridden in a speech
repair. The disfluency is the dependent of the repair.
Jděte dopra- doleva . \n Go to-the-righ- to-the-left .
reparandum(doleva, dopra-)
reparandum(to-the-left, to-the-righ-)
root
: root
The root
grammatical relation points to the root of the sentence. A
fake node ROOT
is used as the governor. The ROOT
node is indexed
with 0, since the indexing of real words in the sentence starts at 1.
ROOT Miluju indická jídla . \n ROOT I-love Indian food .
root(ROOT-1, Miluju)
root(ROOT-7, I-love)
Since release 1.2 of the Czech UD treebank,
there is just one node with the root
dependency relation in every
tree. If the main predicate is not present (due to
ellipsis)
and there are
multiple orphaned dependents, the dependent that is highest in the obliqueness hierarchy is promoted to
the head (root) position and the other orphans are attached to it.
An example sentence-like segment that lacks the main verb: A co na to [říká] MF? “And what [does] MF [say] to it?”
ROOT A co na to MF ? \n ROOT And what to it MF ?
root(ROOT-1, MF-6)
root(ROOT-9, MF-14)
orphan(MF-6, co)
orphan(MF-14, what)
orphan(MF-6, to-5)
orphan(MF-14, it)
case(to-5, na)
case(it, to-12)
cc(MF-6, A)
cc(MF-14, And)
punct(MF-6, ?-7)
punct(MF-14, ?-15)
vocative
: vocative
The vocative
relation is used to mark the dialogue participant addressed in text.
The relation links the addressee’s name or title to its host sentence.
In Czech, the addressee’s name must also appear in the vocative cs-feat/Case form.
Pane , jste blázen ! \n Sir , you-are a-fool !
vocative(blázen, Pane)
vocative(a-fool, Sir)
cop(blázen, jste)
cop(a-fool, you-are)
xcomp
: open clausal complement
An open clausal complement (xcomp
) of a verb or an adjective is a
predicative or clausal complement without its own subject. The
reference of the subject is necessarily determined by an argument
external to the xcomp (normally by the subject of the next higher
clause). This is often referred to as obligatory control.
These complements are always non-finite, and they are
complements (arguments of the higher verb or adjective) rather than
adjuncts/modifiers, such as a purpose clause. The name xcomp
is
borrowed from Lexical-Functional Grammar.
Nechceš si zaplavat ? \n Don't-you-want yourself to-swim ?
xcomp(Nechceš, zaplavat)
xcomp(Don't-you-want, to-swim)
Začal jsem tam pracovat včera . \n Started I-have there to-work yesterday .
xcomp(Začal, pracovat)
xcomp(Started, to-work)
Note that the above condition “without its own subject” does not mean that a
clause is an xcomp
just because its subject is not overt.
The subject must be necessarily inherited from a fixed position in the higher clause.
That is, there should be no available interpretation where the subject of the lower clause may be distinct
from the specified role of the upper clause.
In cases where the missing subject may or must be distinct from a fixed role in the higher clause,
ccomp should be used instead, as below. This includes cases of arbitrary subjects and anaphoric control.
Šéf nařídil vykopat příkop . \n Boss ordered to-dig ditch .
ccomp(nařídil, vykopat)
ccomp(ordered, to-dig)
Pro-drop languages have clauses where the subject is not present as a separate word,
yet it is inherently present (and often deducible from the form of the verb)
and it does not depend on arguments from a higher clause.
Thus in neither of the following two Czech examples is there any overt subject,
yet only the second example contains an xcomp
.
Píšu , protože jsem to slíbil . \n I-write , because I-have it promised .
advcl(Píšu, slíbil)
advcl(I-write, promised)
Slíbil jsem psát . \n Promised I-have to-write .
xcomp(Slíbil, psát)
xcomp(Promised, to-write)


--- title: Geo-locate Emails - part-2 - Locating IP tags: meta: abstract: This is continued article on Geo-locating emails. In the earlier post we saw how to locate the senders IP address and now we will be using that IP Address to find the location of the sender. In this example, I'll be using two sites that enable us to geo-locate IP. thumbnail: date: 2006-09-26T00:02 ---
< Continued from Part 1: Geo-locate Emails
Here, I would not be explaining the exact technical process for this. Instead I will be showing how to geo-locate the IP address. I have been using two sites for the same.
VisualRoute - http://www.visualroute.com/
GeoIPTool - http://www.geoiptool.com/
First we will take up the latter one [GeoIPTool - http://www.geoiptool.com/]. This is easy to use and only for locating the IP address’s geographical location.
Just input the IP address that you found into the “Host/IP” field in the form and click “View Info”. It will show you the geographical location where IP belongs.
Suppose I found the Sender’s IP address as “<IP_ADDRESS>”. The following shows me the information about the IP address.
Fig 2 - Image from GeoIPTool
Still not satisfied!
Well, GeoIPTool does not satisfy my requirements enough. Why? As shown in the image above, it gives me information like the IP address comes from the city Bangalore in Karnataka, India. But then Bangalore is a big city. Briefly speaking, it just does not help me too much.
Therefore I turned to an old method of tracing it through “Visualware’s VisualRoute”.
Currently, the website gives a live demo at VisualRoute- http://visualroute.visualware.com/
Fig 3 - Image from VisualRoute
As the above image shows, the IP address is from “Infosys Technologies Ltd, Bangalore”
But along with it, the tool also gives other not-so-important-for-us information like
How the IP is reached? The complete path from the VisualRoute servers to that IP address.
It also mentions “Bharti Infotel Ltd”. It means that the company “Infosys, Bangalore” uses a connection line from “Bharti Infotel Ltd”. Well, this doesn’t mean that it is the only connection provider, but then strategically it is one of the 2 or may be at most 3 providers.
It mentions “Singtel Exchange”. Well, this is one of the major Internet line providers for India. But the entry here shows that “Bharti Infotel Ltd” mainly uses the “Singtel line” for the “WAN IP pool for Bangalore”. The “Snap” button in the demo allows you to see the text-only report.


chapter 1: Introduction
1.1 Modules and Interfaces
1.2 Tools and Software
1.3 Data Structures for Tree Languages
each
Stm
is a statement
each
Exp
is an expression
s
1
;s
2
executes statement s
1
, then s
2
i:=e
evaluates the expression e, then store the result in variable i


Upload files.
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Ocamlary.IncludeModuleType
This comment is for IncludeModuleType.
IncludeModuleType
This comment is for include EmptySigAlias.
include EmptySigAlias


AboutController
controllers
AboutStage
stages
AppNotification
gui
AppNotificationType
gui
acinq
fr
addError
SplashController
addLog
SplashController
addNotification
NotificationsController
akka
root
amount
ReceivePaymentController
amountDecRegex
GUIValidators
amountError
ReceivePaymentController
amountField
SendPaymentController
amountRegex
GUIValidators
amountUs
ChannelPaneController
announcement
ChannelInfo


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{HEADER-MESSAGE-L4VTRRHMppErK8V07Bkq},
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We thought your writing piece was written fairly well, although there is always room for improvement. Check out some of our suggestions on how to improve your writing.
{USER-WORK-s6ZG5rnRHt4Ydg9O2fv7}
Suggestions
{SUGGESTION-LIST-CWwbXpU8BUyEdAYULIrC}
Sincerely,
英語 (Eigo)
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Last April I posted a plea for help. For thirty years I had been incapable of deciphering part of the lyric of Capitalist Music's titanic masterpiece “Jane's Gone To France”. Several readers tried, but failed, to work out what on earth the great Steve Bloch was harping on about, and were equally as baffled as me.
Now, out of the blue, someone called Matt has added to the comments on that post, and provided the answer. And of course, when you listen to the song again, knowing what Matt has told you, it seems absolutely clear, and indeed obvious.
I doubt that any of you care very much, but this has made me happy. Thank you, Matt.


utilities¶
related to classifiers¶
geocoding.clf_utilities.
create_clf_params_product_generator
(params_grid)[source]¶Generates all possible combinations of classifier’s hyperparameters values.
- Parameters
params_grid (dict) – Contains classifier’s hyperparameters names as keys and the correspoding search space as values
- Yields
dict – Contains a classifier’s hyperparameters configuration
geocoding.clf_utilities.
evaluate
(y_test, y_pred)[source]¶Evaluates model predictions through a series of metrics.
- Parameters
y_test (numpy.ndarray) – True labels
y_pred (numpy.ndarray) – Predicted labels
- Returns
Contains metrics names as keys and the corresponding values as values
- Return type
dict
geocoding.clf_utilities.
get_predictions
(model, X_test)[source]¶Makes predictions utilizing model over X_test.
- Parameters
model (object) – The model to be used for predictions
X_test (numpy.ndarray) – The test features array
- Returns
Contains predictions in (label, score) pairs
- Return type
list
geocoding.clf_utilities.
inverse_transform_labels
(encoder, preds)[source]¶Utilizes encoder to transform encoded labels back to the original strings.
- Parameters
encoder (sklearn.preprocessing.LabelEncoder) – The encoder to be utilized
k_preds (list) – Contains predictions in (label, score) pairs
- Returns
Contains predictions in (label, score) pairs, where label is now in the original string format
- Return type
list
geocoding.clf_utilities.
is_valid
(clf_name)[source]¶Checks whether clf_name is a valid classifier’s name with respect to the experiment setup.
- Parameters
clf_name (str) – Classifier’s name
- Returns
Returns True if given classifier’s name is valid
- Return type
bool
geocoding.clf_utilities.
normalize_scores
(scores)[source]¶Normalizes predictions scores to a probabilities-like format.
- Parameters
scores (list) – Contains the predictions scores as predicted by the model
- Returns
The normalized scores
- Return type
list
geocoding.clf_utilities.
train_classifier
(clf_name, X_train, y_train)[source]¶Trains a classifier through grid search.
- Parameters
clf_name (str) – Classifier’s name to be trained
X_train (numpy.ndarray) – Train features array
y_train (numpy.ndarray) – Train labels array
- Returns
The trained classifier
- Return type
object
related to OSM¶
geocoding.osm_utilities.
cluster_points
(X)[source]¶Clusters points given in X.
- Parameters
X (numpy.ndarray) – Contains the points to be clustered
- Returns
The predicted clusters labels
- Return type
numpy.ndarray
geocoding.osm_utilities.
download_cell
(cell, fpath)[source]¶Downloads cell from Overpass API, writes results in fpath and then parses them into a pandas.DataFrame.
- Parameters
cell (list) – Contains the bounding box coords
fpath (str) – Path to write results and then to read from in order to parse them
- Returns
Contains all street elements included in cell
- Return type
pandas.DataFrame
geocoding.osm_utilities.
extract_streets
(points, path)[source]¶A wrapper function that administrates the streets download.
- Parameters
points (numpy.ndarray) – Contains the data points that define the area to extract from Overpass API
path (str) – Path to write
- Returns
None
geocoding.osm_utilities.
get_clusters_bboxes
(X, labels)[source]¶Extracts a bounding box for each one of the clusters.
- Parameters
X (numpy.ndarray) – Contains the clustered points
labels (numpy.ndarray) – Contains the cluster label for each point in X
- Returns
Contains the cluster labels as keys and the corresponding bounding box as values
- Return type
dict
related to features¶
geocoding.features_utilities.
create_test_features
(df, in_path, scalers_path, out_path, features=None)[source]¶Creates all the included test features arrays and saves them in out_path.
- Parameters
df (pandas.DataFrame) – Contains the test points
in_path (str) – Path to read required items
scalers_path (str) – Path to load required scalers
out_path (str) – Path to write
features (list, optional) – Contains the names of the features to extract
- Returns
The test features array
- Return type
numpy.ndarray
geocoding.features_utilities.
create_train_features
(df, in_path, out_path, features=None)[source]¶Creates all the included train features arrays and saves them in out_path.
- Parameters
df (pandas.DataFrame) – Contains the train points
in_path (str) – Path to read required items
out_path (str) – Path to write
features (list, optional) – Contains the names of the features to extract
- Returns
The train features array
- Return type
numpy.ndarray
geocoding.features_utilities.
encode_labels
(df, encoder=None)[source]¶Encodes target column to with integer values.
- Parameters
df (pandas.DataFrame) – The DataFrame containing the column to be encoded
encoder (sklearn.preprocessing.LabelEncoder, optional) – The label encoder to be utilized
- Returns
pandas.DataFrame: The DataFrame with the encoded column
sklearn.preprocessing.LabelEncoder: The label encoder utilized
- Return type
tuple
geocoding.features_utilities.
filter
(values)[source]¶Filters values by replacing values greater than config.distance_thr with config.distance_thr.
- Parameters
values (list) – Contains distances created by various features
- Returns
Contains the filtered distances
- Return type
list
geocoding.features_utilities.
filter2
(values)[source]¶Filters values by replacing values greater than config.distance_thr with config.distance_thr.
- Parameters
values (list) – Contains distances created by various features
- Returns
Contains the filtered distances
- Return type
list
geocoding.features_utilities.
get_points
(df)[source]¶Builds an array of all points appearing in df. This array will have a shape of (len(df) * number_of_services, 2).
- Parameters
df (pandas.DataFrame) – Contains the data points
- Returns
numpy.ndarray
geocoding.features_utilities.
get_required_external_files
(df, path, features=None)[source]¶Checks if external files are required and if so, downloads them using the Overpass API.
- Parameters
df (pandas.DataFrame) – Contains points in order to define the area to query with Overpass API
path (str) – Path to save the downloaded elements
features (list, optional) – Contains the names of the included features
- Returns
None
geocoding.features_utilities.
load_points_df
(points_fpath)[source]¶Loads points in points_fpath into a pandas.DataFrame and project their geometries.
- Parameters
points_fpath (str) – Path to file containing the points
- Returns
pandas.DataFrame
geocoding.features_utilities.
load_street_gdf
(street_fpath)[source]¶Loads streets in street_fpath into a geopandas.GeoDataFrame and project their geometries.
- Parameters
street_fpath (str) – Path to file containing the streets
- Returns
geopandas.GeoDataFrame
geocoding.features_utilities.
normalize_features
(X, scaler=None)[source]¶Normalize features to [0, 1].
- Parameters
X (numpy.ndarray) – Features array to be normalized
scaler (sklearn.preprocessing.MinMaxScaler, optional) – Scaler to be utilized
- Returns
numpy.ndarray: The normalized features array
sklearn.preprocessing.MinMaxScaler: The scaler utilized
- Return type
tuple
geocoding.features_utilities.
prepare_feats_args
(df, required_args, path)[source]¶Prepares required arguments during features extraction.
- Parameters
df (pandas.DataFrame) – Contains the points for which features will be created
required_args (set) – Contains the names of the required args
path (str) – Path to read from
- Returns
Containing arguments names as keys and their corresponding structures as values
- Return type
dict


TinyColor is a micro framework for inputting colors and outputting colors as different formats. Input is meant to be as permissive as possible.
This plugin contains lists of colors, including colors used in Flat UI, Material, Metro UI.
Read the documentation on the TinyColor project page on github.
View the annotated source code or see the full source on github.
Enter a color:
Or try these: material blue flatui emerald bootstrap4 danger metroui purple flatui greensea 200
And I'll tell you what I know about it:
Developed by Dem Ching.


WITNESS LIST Public Education Committee January 28, 1997-8:00A Budget Overview on Public Education On: Billy G. Jacobs (TEA) Joe Wisnoski (TEA) Mike Moses (TEA) Nancy Vaughan (TEA) Robin Gilchrist (TEA) Registering, but not testifying: On: Jill M. Gray (TEA)


Plugin Information |
|---|
View Syslog logger on the plugin site for more information. |
This plugin send jenkins logs to syslog.
Description
The Jenkins Syslog Logger Plugin send the Jenkins logs to a Syslog Server.
Features:
- Network protocol:
- UDP is currently the only supported network protocol.
- TCP (RFC 6587: Transmission of Syslog Messages over TCP)
- Message delimitation is done with "\r\n" (aka "3.4.2. Non-Transparent-Framing"). Delimitation indicating the message size (aka "3.4.1. Octet Counting") is not implemented for the moment.
- TCP + SSL (RFC 5425: Transport Layer Security (TLS) Transport Mapping for Syslog)
- The certificate of the Syslog server must be trusted by the JVM, no mechanism has been implemented for the moment to provide a specific trust store and/or to disable certificate validation.
- Client Authentication is not supported for the moment.
- Message format: both RFC 3164: The BSD syslog Protocol and RFC 5424: The Syslog Protocol are supported .
Configuration
The Jenkins Syslog Logger Plugin can be configured in the "Configure System" screen.
Basic Configuration
Advanced Configuration
Changelog
Version 1.0.5 (Nov 10, 2015)
- Fix JENKINS-31189 Add setters on configuration for Groovy scripting
Version 1.0.4 (Apr 16, 2015)
- Fix JENKINS-27959 - define UDP as the default transport
Version 1.0.3 (Feb 25, 2015)
- Upgrade syslog-java-client to version 1.0.5 for bug fixes
Version 1.0.2 (Sept 28, 2014)
- Add support for new Syslog transports: TCP and TCP + SSL
Version 1.0.1 (Apr 15, 2014)
- Fix "Infinite loop in java.util.logging when logging is enabled on UdpSyslogMessageSender"
Version 1.0.0 (Apr 7, 2014)
- Initial version
Attachments:
jenkins-syslog-logger-advanced-configuration.png (image/png)
jenkins-syslog-logger-basic-configuration.png (image/png)
jenkins-syslog-logger-basic-configuration.png (image/png)


I am a PhD student in the department of Food Science at Purdue University. My reserach has the broad goal of developing relationships between structural bioinformatic models of pea protein and physical characterization used in food science. This has led to understanding of gels and emulsions critical to meat and dairy analogue production. Techniques include FTIR, DSC, Zeta potential, SDS-PAGE, and rheology. Physical modifications are conudcted using extrusion at sub zero temperatures. Coding and data analysis includes Python, RStudio, and Chimera. Prior to my time at Purdue University, I worked in lean six sigma based process improvement where I obtained by black belt in lean six sigma.
In this role, I work on a USDA NIFA funded grant to study cold denaturation and modeling structures of pea protein. This has now led to two publications on this topic, as well as one on text mining and natural language processing using Python.
Working with Bayer Crop Science, a team of 20 graduate and undergraduate students worked together to analyze a large data set provided by our corporate partner. This involved signifcant data clean up, imputation, and deep learning achriectures, all with the ultimate goal of bringing value and understanding to the goals of Bayer Crop Science.
In Commerce City, I worked as the third shift supervisor, overseeing the production of 100+ loaves of bread and 800+ buns per minute, and leading a team of 25+ produciton assocaites, in addition to working with the engineering and sanitation night crews. I also obtained my black belt in lean six sigma here, which involved developing a Kanban based mixing system including and app development for waste reduction. Annualized project savings: $185,000. I also oversaw corporate compliance of 12 programs including development of time saving tools. My work moved our plant ranking from bottom 5 in terms of safety, compliance, and food safety to top five over the course of a 5 month implmentation.
Worked in three bakeries as a core team member of lean six-sigma improvement projects. My projects indcluded formulation optimization, absorption optimization, line balance, scaling weight reduction, changeover reduction, statistical process control implmentation, process mining, and data anlaysis. This role was a leadership development position including training with McKinsey and Co on lean six-sigma applications, Coprorate Ovations training, and Bread University for bakery process and ingredient functioanlity.
I had five internships, four summer internships and one full year internship at four companies including Flowers Foods inc., Custom Foods inc., Whole Foods, and Engrain. My projects included development and scale up of a multigrain bun for Burger King, Quality Control development and implmentation, moisture retention and shelf life optimization in Burger King buns, custom pretzel line for Ameristar Casinos, development and implmentation of weekly english as a second language classes, enzyme formulation and optimization for a multinational clientele.
GPA: 3.91
GPA: 3.76
In the time I have outside of the labratory and coding, I love to run and bike. I have run a marathon, biked 100+ miles, and I'm always training for some new event. The driving motivation for this exercise is so that I can eat everything that I love to cook!
Other than exercise, love to spend time cooking with my family, coming up with new and creative dishes. We also enjoy travel, both big and small, to check out new restaurants, hiking trails, and anything else we can stumble upon.


Our trip to the Alps
The European languages are members of the same family. Their separate existence is a myth. For science, music, sport, etc, Europe uses the same vocabulary. The languages only differ in their grammar, their pronunciation and their most common words.
Everyone realizes why a new common language would be desirable: one could refuse to pay expensive translators. To achieve this, it would be necessary to have uniform grammar, pronunciation and more common words.
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam.
If several languages coalesce, the grammar of the resulting language is more simple and regular than that of the individual languages. The new common language will be more simple and regular than the existing European languages. It will be as simple as Occidental; in fact, it will be Occidental.
- The European languages are members of the same family.
- Their separate existence is a myth.
- For science, music, sport, etc, Europe uses the same vocabulary.
The European languages are members of the same family. Their separate existence is a myth. For science, music, sport, etc, Europe uses the same vocabulary. The languages only differ in their grammar, their pronunciation and their most common words.
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The European languages are members of the same family. Their separate existence is a myth. For science, music, sport, etc, Europe uses the same vocabulary. The European languages are members of the same family. Their separate existence is a myth.
Europe uses the same vocabulary. The European languages are members of the same family. Their separate existence is a myth.
The European languages are members of the same family. Their separate existence is a myth. For science, music, sport, etc, Europe uses the same vocabulary. The European languages are members of the same family. Their separate existence is a myth.


DAY I BECAME A WOMAN, THE (ROOZI KHE ZAN SHODAM) (director/writer: Marzieh Meshkini; screenwriter: Mohsen Makhmalbaf; cinematographers: Ebrahim Ghafoori/Mohammad Ahmadi; editors: Shahrzad Poya/Maysam Makhmalbaf; cast: Fatemeh Cheragh Akhtar (Girl, Hava), Hassan Nabehan (Boy, Hassan), Shahrbanou Sisizadeh (Mother), Ameneh Pasand (Grandmother), Shabnam Toloui (Woman, Ahoo), Cyrus Kahouri Nejad (Husband), Mahram Zeinal Zadeh (Osman), Nourieh Mahiguirian (Rival Cyclist), Azizeh Seddighi (Old Woman, Houra), Badr Irouni Nejad (Young Boy); Runtime: 78; Shooting Gallery; 2000)
Reviewed by Dennis Schwartz
In Marzieh Meshkini's beautiful Iranian film set on Kish Island, in southern Iran, "The Day I Became a Woman," there are three interlinking stories (allegories) about the three different stages of a woman's life (childhood, young womanhood, an old age). In the first story entitled Hava (Akhtar-Hava is Persian for Eve), a girl of nine reluctantly becomes a woman and must from now on wear a chador (shawl) in public and can't play alone with boys anymore. The first part of the film is grounded in reality, telling about the child's hardship of so suddenly losing her innocence.  This story is very colorfully photographed and has very little dialogue. The final story is surreal and is very comical.  The beach and the seaside is an integral setting for each of the stories. It should also be noted that all the heroines are determined to get what they want but the first two are not in a position to win their fight; but, it is only Ahoo who is forced physically to accept her fate by men.
Marzieh Meshkini is the wife of noted Iranian film director Mohsen Makhmalbaf. He served as the film's screenwriter. The three stories reflect how women suffer from repression because of the strict observance of Muslim law. In the third story when a woman finally has gained her freedom, she doesn't know what to intelligently do with her newly found freedom as she becomes overwhelmed with consumerism. Her freedom comes too late for her. Even though they are different women depicted, it might as well be the same woman because the film is a metaphor for all Iranian women.
The first episode opens with the cute little girl Hava wanting to go outside and play with her friend Hassan (Nabehan). Her mother (Sisizadeh) and granny (Pasand) decide that since she was born at noon and it is now 11 a.m., she could play for one more hour before putting on her chador. To know the exact time they give the girl a stick and tell her to put it in the sand, and when she can't see the stick's shadow it is time to come home. Just before her time is up, she shares a lollipop with Hassan. Strangely enough, the Iranian censors wanted the director to cut this part because they found it too erotic. They had no other objections to the film.
In the second episode Ahoo, with a number of other women, is in a bike race on a bicycle path (the only one in Iran where women are allowed to ride) -- with the Persian Gulf to their side; all the women are wearing their black chadors and full black dresses. Her frantic husband comes by on a horse and insists she quit the race. He says if she doesn't, he will divorce her. When she continues racing the Mullah who introduced her to her husband and married her, rides by her on his horse to urge her to obey her husband. He tells her she is riding on "the devil's mount." When this also fails to stop her from pedaling furiously on, members of her tribe on horseback yell out to tell her how she has disgraced their family. She is finally stopped by her two brothers, who are also on horseback. They take her bike; but, we do not know if she takes another rider's bike and continues on or if she was forced to stop. We know exactly how she feels and how determined she is not to be bullied by them, as we clearly see her animated facial expressions. In any case, it is evident from this episode that a divorce can be attained quite easily by a man.
In the last episode the elderly widow Houra gets off the airplane and is placed in a wheelchair, where a young black boy wheels her to the bazaar. On her fingers she has tied on different colored strings so she can remember all the items she wants. She goes on a shopping spree and purchases everything she always wanted to have in her house: a modern stove, a refrigerator, a kitchen table, a sofa, a new bed, and so. Her main problem is that she has forgotten what the red string is for, and hesitates to leave without trying to figure out what she has forgotten to purchase. A group of boys wheel the stuff to the beach in a parade from the bazaar and deposit it on the beach. When she returns to the store to return a tea kettle, the boys fool around with her goods. On her return from the bazaar she has the boys load it onto rafts to take it to a boat out at sea, where she will return home to feed her rooster (that was her other main concern). Because of her age she doesn't have to wear the chador anymore. But this freedom has come too late, she has lost her beauty and offers no more temptations to the men. Hava can be seen in the last shot of the film wearing her chador, as she has returned to the beach to watch the old lady departing Kish. Perhaps there can be detected a knowing look in her eyes of what the future holds for her.
This is an irresistible film. I ate it up as quickly as I used to lick an ice cream cone when I was a kid. You can learn more about Iran from watching a movie like this one than you can by following the news stories reported by the media.
REVIEWED ON 5/26/2001 GRADE: A-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
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“What do we now see? Midnight!—the blackness of darkness!—Nothing! Where is the wall we were lately elbowing out of the way? It has vanished!—It is lost! We are walled in by darkness, and darkness canopies us above. Look again;—Swing your torches aloft! Aye, now you can see it; far up, a hundred feet above your head, a grey ceiling rolling dimly away like a cloud, and heavy buttresses, bending under the weight, curling and toppling over their base, begin to project their enormous masses from the shadowy wall. How vast! How solemn! How awful!  — Alexander Clark Bullitt, Rambles In The Mammoth Cave During The Year 1844


---
layout: post
authors: ["Greg Wilson"]
title: "Summary of May 2014 Lab Meeting"
date: 2014-05-24
time: "22:00:00"
tags: ["Community", "Lab Meeting"]
---
Our monthly online lab meeting took place this past Thursday (May 22),
and for the first time it included voting on pull requests and other issues.
All the notes from the Etherpad are included below,
but the high points are:
-
The Mozilla Science Lab is hiring
a developer and a community manager.
-
People and projects that would like to take part in our global sprint on July 22-23
are invited to sign up on
this Etherpad.
We won't just work on Software Carpentry curriculum and tooling:
related projects are welcome to use this opportunity
to bring their communities together as well.
-
Arliss Collins and others
will try to simplify the workflow for creating and managing bootcamps.
(Right now, we rely on five different online systems,
and most of the administrators we work with at host institutions
can't make heads or tails of them.)
If you would like to help,
please let us know.
-
We will add people who want to be helpers at bootcamps to our
instructors
mailing list
rather than creating yet another list for reaching them.
-
We voted on the following pull requests:
-
Extra lessons will go under existing directories (e.g.,
novice/git/
)
rather than in a top-level extras
directory.
-
We won't try to standardize usage of "parameter" and "argument",
since most instructors use them idiosyncratically and/or interchangeably.
-
We will incorporate the new lessons on Mercurial as soon as they're done.
-
We will merge the lessons on scikit-learn, Python string formatting, common Python error messages, and setting up SSH keys for GitHub.
-
We won't include the lesson on tmux—people felt it was too specialized—and will ask that the lesson on text data mining in the shell be re-worked.
-
We also voted on the following proposals for new lessons:
-
Using Excel properly, using Make to manage data pipelines, and regular expressions were all approved.
-
The draft lesson on creating and syndicating data on the web was deferred
(only a few people had looked at it).
-
People liked the idea of lessons on statistics with Pandas and managing geospatial data,
but we will need volunteers to take the lead.
Our next lab meeting,
on June 26,
will primarily be devoted to planning for our July 22-23 sprint.
We look forward to seeing lots of you at both.
Agenda
- Announcements
- Votes on pull requests
- Votes on possible additions
- Plans for the July 22-23 sprint (https://etherpad.mozilla.org/swc-sprint-2014 )
- Tracking bootcamp tasks
- Adding helpers to the 'instructors' list
- How to engage departments?
- SWC Administration Tooling and Processes (see note below and line 88)
Attendees
- Greg Wilson (Mozilla, Toronto)
- Xu Fei (New York, NY)
- Paul Wilson (U. Wisconsin-Madison)
- Mike Jones(Pittsburgh, PA)
- Devasena Inupakutika (Southampton, UK)
- Trevor King (Olympia, WA)
- Jason Williams (Cold Spring Harbor Laboratory)
- Damien Irving (University of Melbourne)
- Hamid Mokhtarzadeh (University of Minnesota, MN)
- Dan Warren (Macquarie University, Sydney, Australia)
- Ana Malagon (Yale University, CT)
- Cliburn Chan (Duke University, NC)
- Emily Davenport (UChicago, IL)
- Christian Jacobs (Imperial College London, UK)
- Matthias Bussonnier (institut curie, paris)
- Ethan White (A car somewhere in Indiana)
- Cam Macdonell (Edmonton, Alberta)
- Rob Beagrie (Imperial College London)
- John Blischak (University of Chicago)
- Paul Ivanov (UC Berkeley)
- Gabriel Devenyi (McMaster)
- Jessica Ruyle (University of Oklahoma)
- Doug Latornell (UBC, Vancouver)
- Marian Petre (Open University, UK)
- Francois Michonneau (University of Florida)
- Jeramia Ory (King's College)
- Brad Taber-Thomas (Penn State)
- Brian Glanz (Honolulu)
- Pauline Barmby (Western U, London Ont)
- Tracy Teal (MSU)
- Gavin Simpson (URegina, Canada)
- Joshua Adelman (University of Pittsburgh)
- Kaitlin Thaney (Mozilla Science Lab)
- Arliss Collins (Mozilla, TO)
- Chris Friedline (VCU, Richmond, VA)
- Will Trimble (Argonne National Laboratory, Chicago)
- Denis Haine (U of Montreal, QC)
- JC Leyder (ESA, Spain)
- Christina Koch (Vancouver)
- April Wright (University of Texas, Austin)
- Massimiliano Picone (Italy)
- Raniere Silva (University of Campinas, Campinas, Sao Paulo, Brazil)
- Remi Emonet (Univ Saint Etienne, France)
- Sarah Simpkin (University of Ottawa, Canada)
- Abigail Cabunoc (OICR)
- Amanda Harlin (University of Oklahoma)
- Matt Gee (University if Chicago)
- Dmitri Novikov (Concordia University, Montreal, Canada)
- Marcello Barisonzi (Montreal)
- Daisie Huang (UBC, Vancouver, Canada)
- Philipp Bayer (University of Queensland, Australia)
- Matt Davis (Synthicity)
Announcements
- The Mozilla Science Lab is looking for a developer and a community manager. For details, please see:
July Sprint - July 22-23
- Planning: https://etherpad.mozilla.org/swc-sprint-2014
- Locations
- Melbourne
- Paris
- London
- Edinburgh
- New York City
- Toronto
- East Lansing MI
- Vancouver
- San Francisco
- Norman, OK
- More would be welcome - please contact us if you'd like to host
- Goals
- Domain-specific hour-long examples
- Tools
- E.g. regular expression widget for the IPython Notebook
- More would be welcome - please contact us if you'd like your project to take part
Votes on Pull Requests
- Where should "extra" lessons go:
- In the topic directory, e.g., under novice/r ?
- In a separate directory, e.g., under extras ?
- Vote: put it under topic (clear majority)
- Stephen Turner: can't make call, but seems to me extra lesson should go in topic, e.g., novice/r/extras/ggplot2--etc..., otherwise /extras could get pretty cluttered
- Evening voting: +1+1+1+1+100+1+10+1+1 in favor of putting stuff under
- And a related decision, where to put materials that teach Git using RStudio? {{site.github_url}}/bc/issues/492
- Vote: put it under R (clear majority)
- Discussion of tmux: {{site.github_url}}/bc/pull/249
- Needs markup updates (ask Raniere for more)
- vote: too advanced for us, but hang onto it until we have a story for "import lesson"
- Using 'parameter' instead of 'argument': {{site.github_url}}/bc/pull/420
- No consensus (morning voting)
- Voting for the evening: A is "parameter for definition, argument for value passed in", B is "only use parameter", C is "only use argument", D is "don't even try"
- Evening voting: (!B)DDDDDDDDDDDAD
- IPython Parallel: {{site.github_url}}/bc/pull/438
- I need to do more here, but have lots of good feedback, can we vote next time?
- Introduction to scikit-learn: {{site.github_url}}/bc/pull/443
- Python string formatting: {{site.github_url}}/bc/pull/457
- Common Python error messages: {{site.github_url}}/bc/pull/461
- Votes: +1 +1 +1 +1+1+1+1+1+1
- Paul Wilson: I haven't looked at PR, is this kind of language-deep?
- Greg Wilson: yes, but we can translate into R and other languages
- Add syllabus to default home page: {{site.github_url}}/bc/pull/494
- Text data mining in the shell: {{site.github_url}}/bc/pull/475
- Votes: +1 0 (looks very similar to shell intro?)(good idea, haven't really looked at materials)(needs work)
- Setting up SSH keys for GitHub: {{site.github_url}}/bc/pull/470
- Votes:+1(amanda harlin) +1 (extras) +1 (extras)+1 +1+1+1+1
- Port version control lesson to Mercurial: {{site.github_url}}/bc/pull/439
- Rendered version for review is at http://eos.ubc.ca/~dlatorne/swc/novice/hg/
- Unresolved discussion re: merge conflicts, file annotation vs. GUI tools
- Need some more opinions on this; just 2 that oppose each other at the moment; see "Default behaviour of hg merge and merge tools" thread in hg@ list
- Votes:+1(extras) +1 +1 +1+1(concepts where it differs from git)+1+1
Votes on New Topics
- using Excel properly
- Morning voting: +1 +1 good for Data Carpentry too-1,0+1+1+1+1 +1+1+1+1+1 +1 0
- Evening voting: +1+1+1(openoffice)+1+1+1+1+10
- using Make to manage pipelines
- Morning voting: +1+1 +1 +1 1 0 +1 +1 0 +1 0 0+1+1+1
- Evening voting: +1+1+1+10+100+1
- regular expressions
- Morning voting: +1+1+1 +1+1+1+1+1 +1 +1 +1+1+1+1+1
- Evening voting: +1+1+1+1+10
- creating web-accessible content/data (?) (see {{site.github_url}}/bc/pull/502 )
- Morning voting: +1 +1 0 +1+1+1+1(intermediate level)+1
- Evening voting: +1+10
- Statistics and Pandas (see {{site.github_url}}/bc/pull/432 and {{site.github_url}}/bc/pull/266 )
- Morning voting: we need someone to take the lead
- keen to contribute but a little too busy to lead atm (rob beagrie)
- ditto to what Rob said (Pauline)
- Chris Friedline can help too
- Happy to help with aligning Pandas material with the R lessons (Gavin Simpson)
- Willing to take lead on Excel --> Pandas (April Wright + Christina Koch and Pauline Barmby?)
- Evening voting:
- Happy to contribute (Cliburn) - e.g. can write lesson on split-apply-combine
- Geospatial data (see {{site.github_url}}/bc/pull/387 )
- I'd be happy to do a very basic lesson on handling GIS data in R (Dan Warren)
- Matt Davis can help with Python map stuff.
Tracking Bootcamp Tasks
- See also Administration Tooling and Process below
- Checklists are useful, but not actionable
- Conclusion: we'll use a Google Doc spreadsheet per bootcamp for the next couple of months and see how that goes
- Easy to set up
- Non-technical university admins and others can drive it
- Other options:
- Automatically populate each bootcamp repo with a bunch of tickets?
- Rob Beagrie: is it easy to populate a new repo with a whole bunch of issues?
- Morning voting: +1 +1+1+1 -1 if university admins have to use it-1, same, -1 for admins
- Github markdown supports to-do lists (see https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments) - could we put a default to-do list in the main bc repo?
- +1+1 checklist in issue -1 if university admins have to use it1, same
- Updates in comments don't give watches notification by email (Raniere already request this feature)
- If you put the list in the main repo it needs a new commit to check things off (which would presumably send an email)
Adding Helpers to the Instructors List
- The 'instructors' mailing list currently includes exactly and only certified instructors
- We don't have any systematic way to track would-be helpers
- Proposal: add them to the 'instructors' list+1+1+1+1
SWC Administration Tooling and Processes
- See also Tracking Bootcamp Tasks above
- Neil Chue Hong: I'd like to raise the issue that we should revisit the issue of improving and streamlining our tooling and processes for administrating Software Carpentry across the world [unfortunately I can't join the call, but I hope that Aleksandra can]
- As Software Carpentry grows, we will need to get more people contributing to the administrative effort to support the growth +1
- The current administration process requires the use of five separate pieces of infrastructure and some parts are not easy for people without a software development background+1
- It also doesn't necessarily scale well (*empirical evidence required*) <- trust me, this is an understatement...+1
- Whilst we have very good guides for hosts, instructors and helpers, we don't yet for administrators
- Proposal:
- we get the current SWC administrators: Amy, Arliss, Aleksandra, Giacomo, others? to note the current benefits and drawbacks of the process for administrating SWC as stands
- we set up a small group to look at how we might improve the administration infrastructure
- we set up a small group to develop a better guide for administrators (with added flowcharts!)
- We currently have a Mozilla colleague pulling together a prototype that should help with instructor matching, and are looking to revamp some of the pre and post bootcamp emails, followups, and discuss how to roll out comprehensively (possibly pre-set for instructors to fine tune and send through the eventbrites, for example).
- Here's the pre-bootcamp email that we'd like to have sent with the install instructions for all events
- Would welcome comments and thoughts. Will be working on post-event mailings, as well. (Feel free to ping me directly at kaitlin@mozillafoundation.org)


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