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

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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

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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

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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

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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.

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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

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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

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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.

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Matching Example Hypothesis Text

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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

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Vertex Cost Model Penalize for each vertex substitution

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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

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

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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.

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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

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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

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Final Cost Model Combine VertexCost and RelationCost

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Matching Example Hypothesis Text

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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

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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

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

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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.

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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

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Thanks! Aria Haghighi Andrew Ng Christopher Manning

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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

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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

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