Verb Valency Frame Extraction Using Morphological and Syntactic Features of Croatian

1
Verb Valency Frame Extraction Using Morphological
and Syntactic Features of Croatian

• Krešimir Šojat, Željko Agic, Marko Tadic
• Department of Linguistics, Department of
  Information SciencesFaculty of Humanities and
  Social Sceinces, University of Zagreb
• ksojat, zagic, marko.tadic_at_ffzg.hr
• FASSBL 7 Conference
• Dubrovnik, Croatia2010-10-05

2
Overview

• What?
• extraction and semi-automatic construction of
  verb valency frames
• How?
• rule-based extraction procedure run on the
  Croatian dependency treebank
• manual assignment of tectogrammatical functors
• inference of rules for assigning functors to
  unseen text
• Why?
• creation of treebank-based verb valency lexicon
• enhancement and enrichment of existing resources

3
Valency frames

• valency frame extraction means to detect all
  possible environments of particular verb as found
  in the treebank
• such an approach aims at fast construction of
  valency frames
• extraction is automatic, no elements of frames
  added manually by human annotators
• such automatically acquired verb valency lexicon
  can serve as a basis for further enrichment and
  enhancement of manually constructed resources,
  either existing or constructed from scratch

4
The treebank

• Croatian Dependency Treebank (HOBS)
• follows the guidelines of the Prague DT
• taken from the Croatia Weekly 100 kw sub-corpus
  of the Croatian National Corpus (HNK)
• XCES-encoded up to the word level
• sentence-delimited, tokenized, manually
  lemmatized and MSD-tagged
• serves as the morphological layer of the treebank
• annotated on the syntactic layer
• approximately 2.700 sentences, 67.000 tokens
• manually assigned syntactic functions
• ca 1.300 sentences double-checked and used in
  this experiment

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The treebank

HR Unija je vec dogovorila neke mjere kako bi pomogla Hrvatskoj.
EN The Union has already arranged some measures in order to help Croatia.
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Extraction algorithm

• the algorithm aims at extraction of verb valency
  frame instances
• for each verb in the treebank sample, it descends
  
• one level down the dependency tree to retrieve
  subjects (Sb), objects (Obj), adverbs (Adv) and
  nominal predicates (Pnom)
• Two levels down to retrieve tokens from the
  previous step introduced by subordinate
  conjunctions (AuxC) or prepositions (AuxP)

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Extraction algorithm

• algorithm illustration
• dogovorila (dogovoriti Pred)
• Unija Ncfsn Sb mjere Ncfpa Obj vec Rt Adv
  kako Css AuxC

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Extraction algorithm

• the first version retrieved predicates only and
  was expanded to retrieve all the verbs from the
  treebank sample
• algorithm adapted to retrieve any verbs found in
  the dependency structure, regardless of their
  respective analytical functions and position
  within the dependency trees
• the adaptation itself is implemented in order to
  raise the recall of the algorithm, while still
  maintaining its precision by not changing the
  simple set of descending rules
• i.e. to retrieve as much verbs as possible given
  the limited size of the treebank sample used in
  the experiment

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Extraction algorithm

• the verb imati (Vmn) is annotated as object
  (Obj)

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Extraction algorithm

• Thus, from each sentence the number of extracted
  frames correspondes to the number of verbs
• one frame for the main clause that captures the
  whole syntactic structure of the sentence
• frames extracted from dependent clauses
• naglasio (naglasiti Vmps-sma Pred)
• Mikuška Np-sn Sb kako-gtimati Css AuxC-gtObj
• imati (imati Vmn Obj)
• stanovništvo Ncnsn Sb korist Ncfsa Obj
• od-gtprojekta Spsg-gtNcmsg AuxP-gtAdv
• kroz-gtekoturizam Spsa-gtNcmsa AuxP-gtAdv

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Functor assignment

• In order to annotate verbal frames we used a set
  of 5 argument functors and functors for 32 free
  modification functors
• Argument functors ACT, PAT, ADDR, ORIG, EFF
• Temporal functors TWHEN, TFHL, TFRWH, THL, THO,
  TOWH, TPAR, TSIN, TTILL
• Locative and directional functors DIR1, DIR2,
  DIR3, LOC
• Functors for causal relations AIM, CAUS, CNCS,
  COND, INTT
• Functors for expressing manner ACMP, CPR, CRIT,
  DIFF, EXT, MANN, MEANS, REG, RESL, RESTR
• Functors for specific modifications BEN, CONTRD,
  HER, SUBS
• 936 frame instances were manually annotated for
  424 different verbs

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Results

• valency frame frequency across verb lemmas

Verb Frequency
biti 188
imati 23
reci 15
dobiti 12
raditi 10
kazati 9
pokazati 8
postati 8
vidjeti 8
dati 7
raditi (en. to work, to do) raditi (en. to work, to do)
Valency frame Frequency
ACT PAT 2
ACT CRIT LOC THL 1
ACT MANN TWHEN 1
ACT MEANS TWHEN 1
ACT PAT TSIN 1
dati (en. to give) dati (en. to give)
Valency frame Frequency
ACT ADDR PAT 4
ACT ADDDR PAT 1
ACT ADDR AIM PAT 1
ACT PAT 1
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Results

• frequency of verb valency frames, i.e. n-tuples
  of tectogrammatical functors

Frame Count Percent
ACT PAT 250 26.71
PAT 157 16.77
ACT PAT TWHEN 30 3.21
ACT MANN PAT 23 2.46
ACT ADDR PAT 20 2.14
ACT LOC 20 2.14
ACT LOC PAT 20 2.14
MANN PAT 17 1.82
ACT CAUS PAT 16 1.71
ACT MANN 13 1.39
LOC PAT 12 1.28
ADDR PAT 11 1.18
Other 347 37.07
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Results

• frames annotated with MSD, analytical functions
  and tectogrammatical functors

djelovati (djeluje Pred) neozbiljno Neozbiljno Rnp Adv MANN odustajanje odustajanje Ncnsn Sb ACT
osloboditi (oslobodili Pred) ACT nikada Nikada Rt Adv THL zloduh zloduha Ncmsg Obj PAT
postati (postali Pred) studij studiji Ncmpn Sb ACT fakultet fakultet Ncmsn Obj PAT
zaustaviti (zaustavio Atr) ACT oni ih Pp3-pa--y-n-- Obj PAT dolina u-gtdolini Spsl-gtNcfsl AuxP-gtAdv LOC
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Results

• Distribution of (MSD, analytical function) pairs
  across tectogrammatical functors
• serves as basis for defining functor assignment
  rules from MSD and analytical function

ACT (Actor) ACT (Actor) ACT (Actor) PAT (Patient) PAT (Patient) PAT (Patient) LOC (Locative) LOC (Locative) LOC (Locative)
A-fun MSD A-fun MSD A-fun MSD
Sb Ncmsn 14.91 Obj Ncfsa 11.25 (AuxP) Adv (Spsl) Ncfsl 21.88
Sb Np-sn 13.50 Obj Ncmsa 9.18 (AuxP) Adv (Spsl) Ncmsl 16.41
Sb Ncfsn 12.87 Pnom Ncmsn 5.69 (AuxP) Adv (Spsl) Npmsl 10.16
Sb Ncmpn 9.89 Obj Ncmpa 4.53 (AuxP) Adv (Spsl) Ncnsl 8.59
Sb Npfsn 5.65 Obj Vmn 4.40 (AuxP) Adv (Spsl) Npfsl 8.59
Sb Pi-mpn--n-a-- 4.71 Obj Ncnsa 3.75 (AuxP) Adv (Spsl) Ncmpl 5.47
Sb Ncfpn 3.30 Obj Ncfpa 3.49 (AuxP) Adv (Spsl) Ncfpl 3.91
Sb Ncnsn 2.98 Pnom Ncfsn 2.72 Adv Rl 3.13
Sb Pi-msn--n-a-- 2.51 (AuxC) Obj (Css) Vmip3s 2.07 Adv Css 1.56
Sb Pi-fsn--n-a-- 1.88 Obj Ncmsn 1.81 (AuxP) Adv (Spsg)Ncmsg 1.56
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Conclusions

• in this experiment we have designed and
  implemented one possible approach
• to semi-automatic extraction of a valency frame
  lexicon for Croatian verbs
• to the refinement of existing lexicons by using
  the Croatian Dependency Treebank as an underlying
  resource
• we have automatically extracted 2930 verb valency
  frame instances and annotated 936 frames
• the distribution of valency frames for each of
  the encountered verbs
• the distribution of analytical functions and
  morphosyntactic tags for each of the
  tectogrammatical functors

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Future work

• the first result enables the enrichment of
  existing valency lexicons, such as CROVALLEX
• the second result enables the implementation of a
  rule-based system for automatic assignment of
  tectogrammatical functors to morphosyntactically
  tagged and dependency-parsed unseen text
• this procedure of automatic detection of valency
  frames will be used also in several other
  projects dealing with factored SMT (e.g. ACCURAT)
• regarding dependency parsing of Croatian by using
  the Croatian Dependency Treebank, we shall
  undergo various research directions in order to
  increase overall parsing accuracy

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Thank you for your attention.
www.accurat-project.eu

• The research within the project ACCURAT leading
  to these results has received funding from the
  European Union Seventh Framework Programme
  (FP7/2007-2013), grant agreement no 248347.