THE VERB ARGUMENT BROWSER
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1 THE VERB ARGUMENT BROWSER Bálint Sass Péter Pázmány Catholic University, Budapest, Hungary 11 th International Conference on Text, Speech and Dialog 8-12 September 2008, Brno
2 PREVIEW A corpus query tool for expressions like... verb subcategorization frames institutionalized phrases light verb constructions idiomatic verbal expressions figures of speech common property: verb + arguments uniform framework Motivation: to help in manually building lexical resources Future work: apply the methodology to other languages
3 1 SENTENCE MODEL 2 VERBAL CONSTRUCTIONS AS COLLOCATIONS 3 USAGE & EXAMPLES 4 APPLICATIONS 5 GENERALIZATION
4 1 SENTENCE MODEL 2 VERBAL CONSTRUCTIONS AS COLLOCATIONS 3 USAGE & EXAMPLES 4 APPLICATIONS 5 GENERALIZATION
5 SENTENCE MODEL Basic unit: simple sentence or clause. A lány váll-at von. the girl shoulder-acc pull. The girl shrugs her shoulder. Clause = verb + set of arguments verb=von NOM=lány ACC=váll verb=shrug SUBJ=girl OBJ=shoulder Positions: defined... syntactically: order (in English) morphologically: case markers (in Hungarian)
6 SENTENCE MODEL Basic unit: simple sentence or clause. A lány váll-at von. the girl shoulder-acc pull. The girl shrugs her shoulder. Clause = verb + set of arguments verb=von NOM=lány ACC=váll verb=shrug SUBJ=girl OBJ=shoulder Positions: defined... syntactically: order (in English) morphologically: case markers (in Hungarian)
7 SENTENCE MODEL in Hungarian: 20 different case markers in English: usually prepositions case marker case abbr. English - nominative NOM word order -t accusative ACC word order -ban inessive INE in-phrase -ról delative DEL from-phrase 1 -ból elative ELA from-phrase 2...
8 EXAMPLES Az emberek az időjárás-ról beszélnek. the people the weather-del talk. People talk about the weather. verb=beszél NOM=ember DEL=időjárás verb=talk SUBJ=people ABOUT=weather Péter fél az ismeretlen-től. Peter fear the unknown-abl. Peter fears of the unknown. verb=fél NOM=Péter ABL=ismeretlen verb=fear SUBJ=Peter OF=unknown
9 EXAMPLES Az emberek az időjárás-ról beszélnek. the people the weather-del talk. People talk about the weather. verb=beszél NOM=ember DEL=időjárás verb=talk SUBJ=people ABOUT=weather Péter fél az ismeretlen-től. Peter fear the unknown-abl. Peter fears of the unknown. verb=fél NOM=Péter ABL=ismeretlen verb=fear SUBJ=Peter OF=unknown
10 EXAMPLES Az emberek az időjárás-ról beszélnek. the people the weather-del talk. People talk about the weather. verb=beszél NOM=ember DEL=időjárás verb=talk SUBJ=people ABOUT=weather Péter fél az ismeretlen-től. Peter fear the unknown-abl. Peter fears of the unknown. verb=fél NOM=Péter ABL=ismeretlen verb=fear SUBJ=Peter OF=unknown
11 FIXED AND FREE POSITIONS Hogy jöttek lét-re az első csillagok? how came existence-sub the first stars? How the first stars came into existence? verb=jön SUB=lét NOM=csillagok verb=come INTO=existence SUBJ=stars fixed position: cannot change the word without changing the meaning free position: can change the word without changing the meaning
12 FIXED AND FREE POSITIONS Hogy jöttek lét-re az első csillagok? how came existence-sub the first stars? How the first stars came into existence? verb=jön SUB=lét NOM=csillagok verb=come INTO=existence SUBJ=stars fixed position: cannot change the word without changing the meaning free position: can change the word without changing the meaning
13 MULTI WORD VERBS lét-re jön existence-sub come come into existence multi word verb: verb stem + fixed position(s) separate meaning own argument structure rész-t vesz ban part-acc take INE take part in sg
14 SENTENCE MODEL sentence = verb + set of arguments representation of arguments: position + lemma i.e. verb=jön SUB=lét NOM=csillagok verb=come INTO=existence SUBJ=stars
15 CORPUS PREPARATION Input: Hungarian National Corpus (POS-tagged and disambiguated) clause detection regexps based on conjunction and punctuation patterns verb normalization e.g. separated verbal prefixes attached noun phrase chunking case and lemma of the head of argument phrases representation according to the model
16 1 SENTENCE MODEL 2 VERBAL CONSTRUCTIONS AS COLLOCATIONS 3 USAGE & EXAMPLES 4 APPLICATIONS 5 GENERALIZATION
17 VERBAL CONSTRUCTIONS AS COLLOCATIONS We search for collocations in the space of these structures: verb=jön SUB=lét NOM=csillagok verb=come INTO=existence SUBJ=stars IDEA Apply an association measure taking... the lemma in one particular position as one unit, all other parts of the verb frame as the other unit of the collocation.
18 VERBAL CONSTRUCTIONS AS COLLOCATIONS We search for collocations in the space of these structures: verb=jön SUB=lét NOM=? verb=come INTO=existence SUBJ=? IDEA Apply an association measure taking... the lemma in one particular position as one unit, all other parts of the verb frame as the other unit of the collocation.
19 VERBAL CONSTRUCTIONS AS COLLOCATIONS The Verb Argument Browser can answer the following typical research question: What are the salient words which can appear in a free position of a given verb frame? What are the most important collocates of a given verb (or verb frame) in a particular morphosyntactic position? Association measure: salience (adjusted mutual information) f (x, y) S(x, y) = log 2 f (y) log 2 N f (x) f (y)
20 VERBAL CONSTRUCTIONS AS COLLOCATIONS Important property of the Verb Argument Browser: It can treat not just a single word but a whole verb frame (a verb together with some arguments) as one unit in collocation extraction. It can collect... salient subjects of a verb, salient objects of a given verb subject pair, salient locatives of a given verb subject object triplet...
21 1 SENTENCE MODEL 2 VERBAL CONSTRUCTIONS AS COLLOCATIONS 3 USAGE & EXAMPLES 4 APPLICATIONS 5 GENERALIZATION
22 USAGE Hungarian National Corpus integrated (187 million running words) response times: a few seconds
23 USAGE Hungarian National Corpus integrated (187 million running words) response times: a few seconds
24 USAGE Hungarian National Corpus integrated (187 million running words) response times: a few seconds
25 USAGE Hungarian National Corpus integrated (187 million running words) response times: a few seconds
26 USAGE Hungarian National Corpus integrated (187 million running words) response times: a few seconds
27
28 Query: kér t tól ask ACC ABL ask sy sg verb=kér ABL=? ACC=? verb=ask INDIR=? OBJ=?
29 Query: kér t tól ask ACC ABL ask sy sg verb=kér ABL=? ACC=? verb=ask INDIR=? OBJ=? Result: (Most salient direct objects:) bocsánat forgiveness segítség help elnézés also forgiveness engedély permission...
30 Query: kér t tól ask ACC ABL ask sy sg verb=kér ABL=? ACC=? verb=ask INDIR=? OBJ=? Result: (Most salient direct objects:) bocsánat forgiveness segítség help elnézés also forgiveness engedély permission... for English? question favour...
31 Query: vesz figyelem-ba t take consideration-ill ACC take sg into consideration verb=vesz ILL=figyelem ACC=? verb=take INTO=consideration OBJ=?
32 Query: vesz figyelem-ba t take consideration-ill ACC take sg into consideration verb=vesz ILL=figyelem ACC=? verb=take INTO=consideration OBJ=? Result: (Most salient direct objects:) szempont aspect érdek interest vélemény opinion... for English? Probably the same.
33 Query: ad t give ACC give sg verb=ad verb=give ACC=? OBJ=?
34 Query: ad t give ACC give sg verb=ad verb=give ACC=? OBJ=? Result: (Most salient direct objects:) hang voice to give voice to sg hír news to give news to report igaz true to give true to take sy s side... multi word verbs
35 Query: üt strike NOM sg strikes verb=üt verb=strike NOM=? SUBJ=?
36 Query: üt strike NOM sg strikes verb=üt verb=strike NOM=? SUBJ=? Result: (Some salient subjects:) óra clock The clock strikes twelve. forint 10 Ft strikes his palm. He receives 10 Ft. kő stone Üsse kő! Let a stone strike it! It does not matter.... multi word verbs, figures of speech
37 COLLECTING MWVS Important property of the Verb Argument Browser: Investigating a specific position, the tool provides constructions with this position fixed if there is any such construction (e.g. light verb constructions, idiomatic verbal expressions, figures of speech). kick + OBJ bucket eat + OBJ some kinds of food Verbal expressions with fixed position(s) are frequent, they are not to be ignored, they should be included in language models.
38 1 SENTENCE MODEL 2 VERBAL CONSTRUCTIONS AS COLLOCATIONS 3 USAGE & EXAMPLES 4 APPLICATIONS 5 GENERALIZATION
39 APPLICATIONS lexical database development of a Hungarian to English machine translation system searching for MWVs to include them into the Hungarian WordNet lexicography language teaching
40 FUTURE WORK We are planning to create a Hungarian verb frame frequency dictionary based on this tool. If you specify a verb frame, the Verb Argument Browser tells which are the important lemmas in a chosen position. QUESTION How to collect automatically all important constructions of a verb?
41 1 SENTENCE MODEL 2 VERBAL CONSTRUCTIONS AS COLLOCATIONS 3 USAGE & EXAMPLES 4 APPLICATIONS 5 GENERALIZATION
42 GENERALIZATION The database can be anything which fits the model: a bigger unit which has positions and these positions can be filled by particular items. It is possible to use the methodology to investigate argument structure of adjectives or nouns. The sentence model is in essence language independent. The methodology can be extended to other languages, if a shallow parsed, adequately processed corpus is available.
43 SUMMARY Verb Argument Browser sentence model + collocation extraction important verbal constructions language independent methodology available for Hungarian: (username: tsd; password: vab)... other languages? Contact: sass.balint@itk.ppke.hu
44 SUMMARY Verb Argument Browser sentence model + collocation extraction important verbal constructions language independent methodology available for Hungarian: (username: tsd; password: vab)... other languages? Contact: sass.balint@itk.ppke.hu Thank you for your attention!
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