1. A Semantic Template for Light Verb Constructions Karine Megerdoomian University of California, San Diego karinem@ling.ucsd.edu

2. 2 Problems of Multiword Expressions (MWE) for NLP Systems Lexical Properties Lack of compositionality Form a single argument structure  List in lexicon Phrasal Properties Can be separated from each other Semi-productive  Analyze in syntax

3. 3 Persian Verbal Predicates Consist of preverbal element(s) and a light verb behaving as a single semantic unit غصّه خوردن شکست دادن خجالت کشیدن زندگی کردن شانه زدن درد گرفتن ازدست دادن به دنیا آمدن Persian complex verbs are one type of Multiword Expression

4. 4 Outline Problems of complex verbs for computational applications New analyses: Decomposing complex verbs Semantic template analysis Applications of machine translation: an Interlingua approach

5. 5 Persian Complex Verbs and Machine Translation Idiomatic meaning دست مانی شروع کرد درد گرفتن hand of Mani start did pain catching ‘Mani’s hand started hurting’ Parsing ambiguity بگفته آسوشيتد پرس ] [ شمار بيکاران افزايش ] يافته است [ Lexical proliferation کليک کردن – ايميل زدن – سيگنال انداختن

6. 6 Persian Complex Verbs and Machine Translation Intervening elements درد نمی گيرد – درد بگيرد – درد نخواهد گرفت کشورهای اسلامی خواستار نقش فزاينده سازمان ملل در عراق شدند “The Islamic countries requested an increasing United Nations role in Iraq.” Internal modification مانی کتک بدی خورد. “Mani was beaten badly.”

7. 7 Persian Complex Verbs and Machine Translation List in Lexicon Advantage: Correct translations Problem: Need to repeat for each language pair Problem: Will miss novel usage

8. 8 Recent Theories Decomposing verbs  Analyze primitive features of meaning [Hale & Keyser 1993; Pustejovsky 1995; Levin & Rappaport- Hovav 1995; Fong, Fellbaum & Lebeaux 2001] Similar work in Persian linguistics [Vahedi-Langrudi 1996; Dabir-Moghaddam 1997; Karimi-Doostan 1997; Haji-Abdolhosseini 2000; Megerdoomian 2002; Folli, Harley and Karimi 2003]

9. 9 Semantic Templates Change of state alternation verbs Inchoative: y BECOME Causative:x CAUSE y BECOME باز کردن x CAUSE - BECOME y باز شدن y BECOME

10. 10 Semantic Templates Activity verbs: x ACT گريه کردن، شنا کردن، کار کردن فکر کردن، Activity verbs are more complex Preverbal noun is a verbal or eventive noun [Megerdoomian (2002), Folli et al (2003) and Karimi-Doostan (to appear)] گريه  [ - ه [ ACT ] vp ] np گريه کردن  x ACT [ - ه [ ACT ] vp ] np

11. 11 Semantic Templates Instrument verbs: جارو زدن – رنگ زدن – چکش زدن – شانه زدن x ACT y with Persian:  x زدن y x زدن y English:  x hammer y x sweep y

12. 12 Structure of the Lexicon Two types of “lexicon” 1. Template Lexicon: contains the underlying and universal semantic/syntactic templates  x CAUS y BECOME 2. Vocabulary: lists the words of the language and maps them to the subparts of the semantic/syntactic templates Persian: شدن =BECOME کردن =CAUS BECOME کردن =ACT English: open=BECOME open=CAUSE BECOME

13. 13 Feature Structure Representation Change of state verbs in the Template Lexicon. open: x CAUSE y BECOME vP EVENT cause ARG x PREDVP EVENT become ARG y STATE

14. 14 Feature Structure Representation Change of state verbs in the Vocabulary. FORM: کردن FORM: شدن EVENT: cause EVENT: become PRED. EVENT: become

15. 15 Translating a Change of State Verb نادر در را باز کرد.   EVENT: causeٍ, PRED.EVENT: become, TENSE: NUMBER: sing, PERSON: 3rd  EVENT: cause PRED.EVENT: become PRED.STATE: open TENSE.NUMBER: 3 rd TENSE.PERSON: sing  open Input Template Lexicon Template Transfer Target

16. 16 Conclusion Emerging research on modeling Persian complex verbs in NLP systems based on a semantic template analysis Advantages: Correct translation without listing each LVC in the lexicon Smaller vocabulary lexicon size Facilitates multilingual translation Interlingua – but based on linguistic theory Mismatch between surface form and underlying meaning is not a problem