1. Robust Textual Inference via Graph Matching Aria Haghighi Andrew Ng Christopher Manning

2. Textual Entailment Examples TEXT (T): A Filipino hostage in Iraq was released. HYPOTHESIS (H): A Filipino hostage was freed in Iraq. Entailed Only Need Lexical Similarity Matching

3. Another Example T: The Psychobiology Institute of Israel was established in 1979. H: Israel was established in 1979. Not Entailed Must go beyond matching only words

4. The Need For Relations H: Israel was founded in 1971. T: The Psychobiolgy Institute of Israel was founded in 1971. No match for important relation in H! Must match words and relations between them

5. Our Approach Dependency Graph Represent words / phrases as vertices and edges as syntactic / semantic relations Graph Matching Approximate notion of Isomorphism H is entailed from T if the cost of matching H to T low.

6. Representation Pipeline Raw Text John’s mother walked to the store. Modified parser of [Klein and Manning ‘03] Handle collocations: John rang_up Mary Phrase Structure Parse PP S NPVP to the store. John’s mother walked

7. Representation Pipeline Phrase Structure Parse PP S NPVP to the store. John’s mother walked Dependency Tree walked (VBD) mother (NN) John (NNP) store (NN) subj poss to Modified Collins’ Head Rules Typed relations via tgrep expressions

8. Representation Pipeline Local dependencies not enough Additional Analysis Semantic Role Labeling [Toutanova et al ‘05] Named Entity Recognition: Collapse named entities into single vertex [Finkel et al ‘04] Coreference Resolution: T: Since its formation in 1948, Israel … H: Israel was established in 1948.

9. Matching Example Hypothesis Text

10. Cost Model Matching: A mapping from vertices of H to those of T (and NULL vertex) Cost of matching H to T determined by lowest cost matching

11. Vertex Cost Model Penalize for each vertex substitution

12. Vertex Substitution VertexSub(v,M(v)) Exact Match Synonym Match Hypernym Match: v is a “kind of” M(v) WordNet Similarity (Resnik Measure) Distributional Similarity Part-Of-Speech Match

13. Vertex Weight Weights for Vertex Importance Part-Of-Speech Named Entity Type TF-IDF

14. Relation Matching Partial Match (and Stem Match) T: The Japanese invasion of Manchuria. H: Japan invaded Manchuria. Ancestor Match T: John is studying French farming practices. H: John is studying French farming.

15. Relation Cost For each edge e in H, is the image under M, a path in T Weigh each edge according to “importance” of typed relation

16. Cost Model PathSub(v v’, M(v) M(v’)) Exact Match: Matching preserves edge and edge label Partial Match: Match preserves edge but not label Ancestor Match: M(v) is an ancestor of M(v’) Kinked Match: M(v) and M(v’) share a common ancestor Costs Scale with Length of Path

17. Final Cost Model Combine VertexCost and RelationCost

18. Matching Example Hypothesis Text

19. Finding Minimal Matching With VertexCost only, minimal matching found with Bipartite Graph Matching NP-Hard: RelationCost(M) = 0 if and only if H isomorphic to sub-graph of T Approximate Search Initialize M to best matching using only VertexCost(M) [Bipartite Graph Matching] Do Greedy Hill-climbing with full cost model Seems to do well in practice

20. Learning Weights Parameterize Substitution Costs Problem: We don’t know matchings in training data. If we did, training would be easy. Solution: Alternate between finding matchings and re-estimating parameters

21. Experiments CWS = Confidence Weighted Score Data: Recognizing Textual Entailment ‘05 [Dagan et al, ‘05] 567 Development Pairs 800 Test Pairs

22. Problem Cases Monotonicity Assumptions Superlatives T: Osaka is the tallest tower in western Japan. H: Osaka is the tallest tower in Japan. Non-Factive Verbs T: It is rumored that John is dating Sally. H: John is dating Sally.

23. Conclusions What’s been done Learned Graph Matching framework New edge and vertex features Fast effective search procedure What’s Needed? More Resources! Lexical Resources: Problems with Recall Better Dependency Parsing Measures of Phrasal Similarity

24. Thanks! Aria Haghighi Andrew Ng Christopher Manning

25. Examples T: C and D Technologies announced that it has closed the acquisition of Datel Inc. H: Datel Acquired C and D technologies. Not Entailed Recognize switch in argument structure. Note nominilization

26. Textual Entailment Problem Definition Given text and hypothesis (T,H) Determine if H ‘follows’ from T ? Not strict logical entailment Applications Information Extraction Question Answering