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Modeling Semantic Containment and Exclusion in Natural Language Inference

Modeling Semantic Containment and Exclusion in Natural Language Inference

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Modeling Semantic Containment and Exclusion in Natural Language Inference

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  1. Modeling Semantic Containment and Exclusion in Natural Language Inference Bill MacCartney and Christopher D. Manning NLP Group Stanford University 22 August 2008

  2. Some Some no Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Natural language inference (NLI) • Aka recognizing textual entailment (RTE) • Does premise P justify an inference to hypothesis H? • An informal, intuitive notion of inference: not strict logic • Emphasis on variability of linguistic expression P Every firm polled saw costs grow more than expected,even after adjusting for inflation. H Every big company in the poll reported cost increases. yes • Necessary to goal of natural language understanding (NLU) • Can also enable semantic search, question answering, …

  3. robust,but shallow deep,but brittle lexical/semanticoverlap Jijkoun & de Rijke 2005 FOL &theoremproving Bos & Markert 2006 patternedrelationextraction Romano et al. 2006 semantic graph matching Hickl et al. 2006 MacCartney et al. 2006 Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion naturallogic (this work) NLI: a spectrum of approaches Solution? Problem:hard to translate NL to FOL idioms, anaphora, ellipsis, intensionality, tense, aspect, vagueness, modals, indexicals, reciprocals, propositional attitudes, scope ambiguities, anaphoric adjectives, non-intersective adjectives, temporal & causal relations, unselective quantifiers, adverbs of quantification, donkey sentences, generic determiners, comparatives, phrasal verbs, … Problem:imprecise  easily confounded by negation, quantifiers, conditionals, factive & implicative verbs, etc.

  4. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Outline • Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion

  5. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion What is natural logic? ( natural deduction) • Characterizes valid patterns of inference via surface forms • precise, yet sidesteps difficulties of translating to FOL • A long history • traditional logic: Aristotle’s syllogisms, scholastics, Leibniz, … • modern natural logic begins with Lakoff (1970) • van Benthem & Sánchez Valencia (1986-91): monotonicity calculus • Nairn et al. (2006): an account of implicatives & factives • We introduce a new theory of natural logic • extends monotonicity calculus to account for negation & exclusion • incorporates elements of Nairn et al.’s model of implicatives

  6. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion 7 basic entailment relations Relations are defined for all semantic types: tiny⊏small, hover⊏fly, kick⊏strike,this morning⊏today, in Beijing⊏in China, everyone⊏someone, all⊏most⊏some

  7. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Entailment & semantic composition • Ordinarily, semantic composition preserves entailment relations: eat pork⊏eat meat, big bird | big fish • But many semantic functions behave differently:tango⊏dance  refuse to tango⊐refuse to danceFrench | German  not French _ not German • We categorize functions by how they project entailment • a generalization of monotonicity classes, implication signatures • e.g., not has projectivity {=:=, ⊏:⊐, ⊐:⊏, ^:^, |:_, _:|, #:#} • e.g., refuse has projectivity {=:=, ⊏:⊐, ⊐:⊏, ^:|, |:#, _:#, #:#}

  8. @ @ ⊐ ⊐ ⊐ @ @ ⊏ ⊏ @ @ Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion @ @ nobody nobody can can without without a shirt clothes enter enter Projecting entailment relations upward • If two compound expressions differ by a single atom, their entailment relation can be determined compositionally • Assume idealized semantic composition trees • Propagate entailment relation between atoms upward, according to projectivity class of each node on path to root

  9. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion A (weak) inference procedure • Find sequence of edits connecting P and H • Insertions, deletions, substitutions, … • Determine lexical entailment relation for each edit • Substitutions: depends on meaning of substituends: cat | dog • Deletions: ⊏ by default: red socks⊏socks • But some deletions are special: not ill ^ ill, refuse to go | go • Insertions are symmetric to deletions: ⊐ by default • Project up to find entailment relation across each edit • Compose entailment relations across sequence of edits • à la Tarski’s relation algebra

  10. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion The NatLog system NLI problem linguistic analysis 1 alignment 2 lexical entailment classification 3 entailment projection 4 entailment composition 5 prediction

  11. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Running example P Jimmy Dean refused to move without blue jeans. H James Dean didn’t dance without pantsyes OK, the example is contrived, but it compactly exhibits containment, exclusion, and implicativity

  12. refuse without JimmyDean move blue jeans Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion + + + – – – + + Step 1: Linguistic analysis • Tokenize & parse input sentences (future: & NER & coref & …) • Identify items w/ special projectivity & determine scope • Problem: PTB-style parse tree  semantic structure! S category: –/o implicatives examples: refuse, forbid, prohibit, … scope: S complement pattern: __ > (/VB.*/ > VP $. S=arg) projectivity: {=:=, ⊏:⊐, ⊐:⊏, ^:|, |:#, _:#, #:#} VP S VP VP PP NP NP NNP NNP VBD TO VB IN JJ NNS Jimmy Dean refused to move without blue jeans • Solution: specify scope in PTB trees using Tregex [Levy & Andrew 06]

  13. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Step 2: Alignment • Alignment as sequence of atomic phrase edits • Ordering of edits defines path through intermediate forms • Need not correspond to sentence order • Decomposes problem into atomic inference problems • We haven’t (yet) invested much effort here • Experimental results use alignments from other sources

  14. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Step 3: Lexical entailment classification • Goal: predict entailment relation for each edit, based solely on lexical features, independent of context • Approach: use lexical resources & machine learning • Feature representation: • WordNet features: synonymy (=), hyponymy (⊏/⊐), antonymy (|) • Other relatedness features: Jiang-Conrath (WN-based), NomBank • Fallback: string similarity (based on Levenshtein edit distance) • Also lexical category, quantifier category, implication signature • Decision tree classifier • Trained on 2,449 hand-annotated lexical entailment problems • E.g., SUB(gun, weapon): ⊏, SUB(big, small): |, DEL(often): ⊏

  15. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Step 3: Lexical entailment classification

  16. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion inversion Step 4: Entailment projection

  17. For example: human ^ nonhuman fish | human fish < nonhuman Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion final answer Step 5: Entailment composition

  18. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion The FraCaS test suite • FraCaS: a project in computational semantics [Cooper et al. 96] • 346 “textbook” examples of NLI problems • 3 possible answers: yes, no, unknown (not balanced!) • 55% single-premise, 45% multi-premise (excluded)

  19. 27% error reduction Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Results on FraCaS

  20. 27% error reduction in largest category, all but one correct high accuracy in sections most amenable to natural logic Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion high precision even outsideareas of expertise Results on FraCaS

  21. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion The RTE3 test suite • Somewhat more “natural”, but not ideal for NatLog • Many kinds of inference not addressed by NatLog:paraphrase, temporal reasoning, relation extraction, … • Big edit distance  propagation of errors from atomic model

  22. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Results on RTE3: NatLog (each data set contains 800 problems) • Accuracy is unimpressive, but precision is relatively high • Strategy: hybridize with Stanford RTE system • As in Bos & Markert 2006 • But NatLog makes positive prediction far more often (~25% vs. 4%)

  23. 4% gain (significant,p < 0.05) Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Results on RTE3: hybrid system (each data set contains 800 problems)

  24. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion Conclusion: what natural logic can’t do • Not a universal solution for NLI • Many types of inference not amenable to natural logic • Paraphrase: Eve was let go = Eve lost her job • Verb/frame alternation: he drained the oil⊏the oil drained • Relation extraction: Aho, a trader at UBS…⊏Aho works for UBS • Common-sense reasoning: the sink overflowed⊏the floor got wet • etc. • Also, has a weaker proof theory than FOL • Can’t explain, e.g., de Morgan’s laws for quantifiers: Not all birds fly= Some birds don’t fly

  25. Introduction • A Theory of Natural Logic • The NatLog System • Experiments with FraCaS • Experiments with RTE • Conclusion :-) Thanks! Questions? Conclusion: what natural logic can do Natural logic enables precise reasoning about containment, exclusion, and implicativity, while sidestepping the difficulties of translating to FOL. The NatLog system successfully handles a broad range of such inferences, as demonstrated on the FraCaS test suite. Ultimately, open-domain NLI is likely to require combining disparate reasoners, and a facility for natural logic is a good candidate to be a component of such a system.