PDT 1

Zdeněk Žabokrtský

e-mail: zabokrtz@cs.felk.cvut.cz

Czech Technical University, Department of Computer Science

the following presentation can be downloaded from http://obelix.ijs.si/ZdenekZabokrtsky/PDT/

PDT 2

The Prague Dependency Treebank (PDT)

• long-term project aimed at a complex annotation of a

part of the Czech National Corpus with rich annotation

scheme

• Institute of Formal and Applied Linguistics

– established in 1990 at the Faculty of Mathematics and

Physics, Charles University, Prague

– Jan Hajič, Eva Hajičová, Jarmila Panevová, Petr Sgall, …

– http://ufal.ms.mff.cuni.cz

PDT 3

The Prague Dependency Treebank

• inspiration:

– the Penn Treebank (the most widely used syntactically

annotated corpus of English)

• motivation:

– the treebank can obviously be used for further linguistic

research

– more accurate results can be obtained when using

annotated corpora than when using texts in their raw form

(unsupervised training)

PDT 4

Source of the text data

• provided by Institute of the Czech National Corpus (ICNC)

• text sample for PDT – 456 705 tokens (words and punctuations) in 26610

sentences, divided into 576 files, 50 sentences per file• 40 % - general newspaper articles (Lidové noviny, Mladá

Fronta)• 20 % - economic new and analysis (Českomoravský profit)• 20 % - popular science magazine (Vesmír)• 20 % - information technology texts

– divided into • a training set (19 126 sentences)• a development test set (3 697) • a cross-evaluation test data set (3 787)

PDT 5

Institute of the Czech National Corpus

• founded 1994 at the Faculty of Philosophy, Charles University,

• head of the institute: prof. František Čermák• 100 million words

• freely accessible: http://ucnk.ff.cuni.cz– querry language CQP (corpus query processor, developed

at the university in Stuttgart)

– regular expressions

– examples of querries:

disku[s|z]e

.+nést

PDT 6

CNC: querry example

• querry: .+nosit

• response:

tačí se trochu vybavit , <nanosit> kupu listí a sena - já ho

ie Každý mistr by se měl <honosit> nějakým rekordem či jedin

anční tísni by měly dítě <donosit> . Bezvýhradná povinnost p

í hladovění bude schopna <donosit> plod . Mimochodem i u sou

evítané těhotenství tzv. <donosit> a dítěte se vzdát ve pros

mž sedíme , nepostavil . <Vynosit> tuny kamení na zádech , t

byl v nebezpečí a naděje <donosit> dítě žádná . Jeden večer

6 - Živit mateř . mlékem <Nanosit> 57 - Ukončit létání 58 -

odstatně větší a může se <honosit> řadou úctyhodných přívlas

vy , v pokoji nekouřit , <nenosit> domů alkohol . Dodržovat

ve městě , které se mělo <honosit> jen svým " dělnickým hnut

ve městě , které se mělo <honosit> jen svým " dělnickým hnut

. . .

PDT 7

Layered structure of PDT

• morphological level– full morphological tagging (word

forms, lemmas, mor. tags)

• analytical level– surface syntax– syntactic annotation using

depencency syntax (captures analytical functions such as Subject, Object,...)

• tectogrammatical level– level of linguistic meaning

(tectogrammatical functions such as Actor, Patient,...)

raw text

morphologicallytagged text

analytic treestructures (ATS)

tectogrammaticaltree structures (TGTS)

PDT 8

The Morphological Level

• a tag and a lemma are assigned to each word form

from the input text

• 3030 tags (Czech is an inflectionally rich language)

• 6 tag variables

– number - degrees of comparison

– case - person

– gender - negation

• example:

– VPS3A - verb (indicative, present tense, sing., 3rd

person, affirmative)

PDT 9

Morphological Analysis

• an automatic process: – input: word form– output: a set of possible lemmas, each lemma

accompanied by a set of possible tags

• currently covers 720000 Czech lemmas, based on 210000 stems

• can recognize 20 million word forms

• output ambiguity:– there may be 5 different lemmas for a given word form– 27 different tags for a given lemma– example: učení - NNS1A, NNS2A, NNS3A,...,NNP5A

PDT 10

The whole process of morphological tagging

• automatic morphological analysis

• manual disambiguation– 2 annotators– in the full text context– special software tool

• automatic comparison

• manual correction

raw text

unambiguously tagged text

PDT 11

Data Format• Standard Generalized Markup Language (SGML)

• a sample of DTD (Document Type Definition) related to the morphological level:

<!ELEMENT MMl - O (#PCDATA & R? & E? & e? & T* & MMt*)

-- lemma (base form), description see the l tag;

machine assigned (by a morphological analysis program),

NOT disambiguated

-->

<!ELEMENT MDl - O (#PCDATA & R? & E? & e? & T* & MDt*)

-- lemma (base form), description see the l tag;

machine assigned (by a tagger), disambiguated

if more than 1: n-best -->. . .

<!ELEMENT MMt - O (#PCDATA)

-- morphological tag(s) as assigned by morphology,

NOT disambiguated

-->

<!ELEMENT MDt - O (#PCDATA)

-- morphological tag(s) as assigned by machine, disambiguated,

possibly also with weight/prob; if more than 1: n-best

-->

PDT 12

Example of tagged sentence • Ty mají pak někdy takovou publicitu, že to dotyčnou kancelář zlikviduje.

<s id=cmpr9415:025-p19s2/bcc14zua.fs/#18>

<f cap>Ty<MMl>ty<MMt>PP2S1<MMt>PP2S5<MMl>ten<MMt>...

... PDFP1<MMt>PDFP4<MMt>PDIP1<MMt>PDIP4<MMt>PDMP4<A>Sb<r>1<g>2

<f>mají<MMl>mít<MMt>VPP3A<A>Pred<r>2<g>0

<f>pak<MMl>pak<MMt>DB<A>Adv<r>3<g>2

<f>někdy<MMl>někdy<MMt>DB<A>Adv<r>4<g>2

<f>takovou<MMl>takový<MMt>AFS41A<MMt>AFS71A<A>Atr<r>5<g>6

<f>publicitu<MMl>publicita<MMt>NFS4A<A>Obj<r>6<g>2

<D>

<d>,<MMl>,<MMt>ZIP<A>AuxX<r>7<g>8

<f>že<MMl>že<MMt>JS<A>AuxC<r>8<g>6

<f>to<MMl>ten<MMt>PDNS1<MMt>PDNS4<A>Sb<r>9<g>13

<f>dotyčnou<MMl>dotyčný<MMt>AFS41A<MMt>AFS71A<A>Atr<r>10<g>11

<f>kancelář<MMl>kancelář<MMt>NFS1A<MMt>NFS4A<A>Obj<r>11<g>13

<f>prakticky<MMl>prakticky_^(*1ý)<MMt>DG1A<A>Adv<r>12<g>13

<f>zlikviduje<MMl>zlikvidovat_:W<MMt>VPS3A<A>Obj<r>13<g>8

<D>

<d>.<MMl>.<MMt>ZIP<A>AuxK<r>14<g>0

PDT 13

The Analytical Level• the dependency structure was chosen to represent the syntactic

relations within the sentence.

• output of the analytical level: analytical tree structure (ATS)

– oriented, acyclic graph with one entry node

– every word form and punctuation mark is represented as a node

– the nodes are annotated by attribute-value pairs

• new attribute: analytical function

– determines the relation between the dependent node and its governing

nodes

– values: Sb, Obj, Adv, Atr,....

PDT 14

Example of ATS• V návrzích na případné změny vycházejí ze svých většinou

několikaletých podnikatelských zkušeností.

PDT 15

Selected attributes of ATS’s nodes

PDT 16

Selected values of the analytical function

PDT 17

Example of tagged sentence• ...ve sledovaném období žádný okres nezlepšil svoji pozici...

<f>ve<MMl>v<MMt>RV4<MMt>RV6<A>AuxP<r>4<g>9

<f>sledovaném<MMl>sledovaný_^(*2t)<MMt>AIS61A<MMt>AMS61A<MMt>ANS61A<A>Atr<r>5<g>6

<f>období<MMl>období<MMt>NNP1A<MMt>NNP2A<MMt>NNP4A<MMt>NNP5A<MMt>NNS1A<MMt>NNS2A<MMt>NNS3A<MMt>NNS4A<MMt>NNS5A<MMt>NNS6A<A>Adv<r>6<g>4

<f>žádný<MMl>ľádný<MMt>PNFIS4<MMt>PNFYS1<MMt>PNFYS5<A>Atr<r>7<g>8

<f>okres<MMl>okres<MMt>NIS1A<MMt>NIS4A<A>Sb<r>8<g>9

<f>nezlepšil<MMl>zlepąit_:W<MMt>VRYSN<A>Pred_Co<r>9<g>11

<f>pozici<MMl>pozice<MMt>NFS3A<MMt>NFS4A<MMt>NFS6A<A>Obj<r>10<g>9

PDT 18

The Tectogrammatical Level

• based on the framework of the Functional Generative

Description as developed by Petr Sgall

• in comparison to the ATSs, the tectogrammatical tree

structures (TGTSs) have the following characteristics:

– only autosemantic words have an own node, function words

(conjunctions, prepositions) are attached as indices to the

autosemantic words to which they belong

– nodes are added in case of clearly specified deletions on the

surface level

– analytical functions are substituted by tectogrammatical

functions (functors), such as Actor, Patient, Addressee,...

PDT 19

Example of TGTS• Podle předběžných odhadů se totiž počítá, že do soukromého vlastnictví

bude prodáno minimálne 10000 bytů

PDT 20

Selected attributes of a TGTS‘s node

PDT 21

Functors

• tectogrammatical counterparts of analytical functions

• about 40 functors in 2 groups:

– actants

• Actor, Patient, Adressee, Origin, Effect

– free modifiers

• LOC, DIR1, RSTR, TWHEN, TTIL,...

• provide more detailed information about the relation

to the governing node than the analytical function

PDT 22

Example of ATS ...• Kdo chce investovat dvě stě tisíc korun do nového automobilu, nelekne

se, že benzín byl změnou zákona trochu zdražen.

PDT 23

... and the corresponding TGTS

PDT 24

Tectogrammatical tagging

• 2 parallel streams

ATS treebank

smaller set of fully tagged TGTSs

larger set of partially tagged TGTSs (only changes of tree structure, functor and TFA assignment)

PDT 25

Problems of automatic functor assignment

• za roh - DIR3

• za hodinu - TWHEN

• za svobodu - OBJ

• po otci

– TWHEN (Přišel po otci.)

– NORM (Jmenuje se po otci.)

– HER (Zdědil dům po otci.)

– . . .

PDT 26

Summary

• the current state of art:– there are several manually annotated files of TGTSs– methods for automatic transformation from ATS into

TGTS form are in development

Czech National Corpus

morphologically tagged corpus

ATStreebank

TGTStreebank

September, 1994November, 1996 March, 2000