Real Arguments: Exploring New Sources of Evidence for Argument Structure

1. Real Arguments: Exploring New Sources of Evidence for Argument Structure Philip Resnik University of Maryland January 7, 2003

2. A Few Personal Lessons • High performance in a specific domain requires manual effort in constructing good domain-specific resources, such as detailed lexical entries. Ex.There’s nothing special about catching a giant. M. Chabon, Summerland, p. 177 • I don’t want to be the one to do it!

3. A Few Personal Lessons • High performance on broad-coverage tasks requires annotation of lots of training data, making “hidden” linguistic representations observable.

4. State of the Art Parsing

5. A Few Personal Lessons • High performance on broad-coverage tasks requires annotation of lots of training data, making “hidden” linguistic representations observable. • I don’t want to be the one to do it!

6. Outline • Introduction • Questions, results, issues • Some current results and works in progress • Parallel text as a source of evidence • The World Wide Web as a source of evidence • Where to from here?

7. The Big Questions(cognitive and otherwise) • What is the relationship between argument structure and surface realization? • How can this relationship be acquired? • How do we explain (or at least deal with) the flexibility of real usage?

8. cause Established Results • Systematicity of semantics/syntax relations • Jackendoff, Levin, Pinker, Dowty, Pustejovsky, Gleitman, Croft, Kemmer, … become be

9. S catch NP VP obj catch catch giant Established Results • Centrality of lexical dependency representations • Collins, Charniak, Joshi, Jelinek, …

10. Burning Issues • Relating theory to real usage • Rampant exceptions to introspective judgments • Unexplored sources of data

11. Outline • Introduction • Questions, results, issues • Some current results and works in progress • Parallel text as a source of evidence • The World Wide Web as a source of evidence • Where to from here?

12. Partially

13. What the Happy People Do • Supervised classification • Probabilistic generative modeling

14. implicit structure x x x x x x x x x x x x x x x x x x Supervised Classification observable classes f2 observable feature representations f1

15. implicit process Probabilistic Generative Modeling observable representations observable representations Come quietly or there will be trouble… source channel P(u) P(o|u)

16. Key Elements of Supervised Approaches • Observable underlying representation • Observable surface representation • Implicit process/model/structure

17. I got a wedding gift for my brother implicit process implicit process meaning meaning meaning nik nire anaiari ezkontza opari bat erosi nion I-erg MY BROTHER-dat WEDDING GIFT a BUY-past The Case of Parallel Translations observable surface representation observable surface representation

18. Hieroglyphic Egyptian Demotic Greek This idea is not without precedent.

19. Key Claim • Can we recover the hidden common meaning? • Probably not. • Can we exploit the hidden common meaning? • Yes. And this will let us take supervised approaches to unannotated data, helping to solve monolingual problems.

20. subj obj I got a wedding gift for my brother meaning nik nire anaiari ezkontza opari bat erosi nion I-erg MY BROTHER-dat WEDDING GIFT a BUY-past Annotation Projectionwith the Direct Correspondence Assumption (DCA) Joint work with Amy Weinberg, Rebecca Hwa, Okan Kolak

21. Dependency Projection Framework bilingual corpus English Chinese projected Chinese dependency treebank English dependency parser word alignment model supervised training unseen Chinese sentences dependency parser Projection dependency trees for unseen sentences

22. Direct Projection Algorithm • If there is a syntactic relationship between two English words, then ensure that the same syntactic relationship also exists between their corresponding words in the second language.

23. Output of the Direct Projection Algorithm mod mod det subj obj det expressed satisfaction regarding this subject The Chinese 中国 方面 对 此 表示 满意 *e* *M* mod mac mod det obj mac subj

24. Revised Projection Algorithm • Exploitation of general linguistic principles • Headness: Chinese is generally head-initial • Development of post-processing rules • Functional/enumerated categories (closed class) • Projected parts of speech • Cf. tsed (Blaheta 2002)

25. Quality of Automatically Annotated Chinese Data

26. General Observations • Limitations of assuming direct correspondence • Linguistic divergences literature (e.g. Dorr 1994) • Transfer based MT (e.g. Han et al. 2000) • But: the DCA works to a surprising extent! • Need better learning from noisy representations • Cf. Yarowsky and Ngai (2001), learning via annotation projection of POS tags, phrase bracketing, etc.

27. Outline • Introduction • Questions, results, issues • Some current results and works in progress • Parallel text as a source of evidence • The World Wide Web as a source of evidence • Where to from here?

28. The WWW as Linguistic Evidence • NSF ITR project: “Using the Web as a Corpus for Empirical Linguistic Research” (Resnik, Fellbaum, Olsen) • Goals: • Make it easy for linguistic theoreticians to develop and verify claims about linguistic behavior using naturally occurring evidence. • Investigate specific phenomena at the lexical semantics/syntax interface. Student credits: Aaron Elkiss, Rafi Khan, Jesse Metcalf-Burton, Usama Soltan

29. Introspective versus Observed • Typical argument: Galileo ignored friction • Counterpoint: Science is the attempt to make the chaotic diversity of our sense-experience correspond to a logically uniform system of thought [in which] experience must be correlated with the theoretical structure… What we call physics comprises that group of natural sciences which base their concepts on measurements… [emph. added] (Einstein, 1940)

30. Using the Web as a Corpus • The “Linguist’s Search Engine” (LSE) • Permits search on syntactic and lexical criteria • Grammatical category • Constituency and dependency relationships • Morphological variation • Semantic (WordNet) argument category • Reports archived attestations (www.archive.org) • Permits examination of context and annotations

31. Example • “Middle” construction • It is easy to cut/swallow the bread. • The bread cuts/*swallows easily. • LSE searches (advanced query language) (VP <1 VBZ <2 (ADVP < (RB < easily))) (VP <1 (VBZ < @VC123) <2 (ADVP < (RB < easily)))

36. Next Steps • Faster searches • Web-scale comprehensiveness • Extended search features • Query by example • Lexical semantics seminar, Spring 2003

37. Big Questions, Burning Issues • How do we explain (or at least deal with) the flexibility of real usage? • How can we acquire knowledge about argument structure from empirical evidence? • What is the relationship between argument structure and surface realization?