Two Related Lexico-Syntactic Approaches to Entailment Vasile Rus

Two Related Lexico-Syntactic Approaches to Entailment Vasile Rus Institute for Intelligent Systems Department of Computer Science http: //www. cs. memphis. edu/~vrus

TODAY- Outline • General strategy – Map T and H into lexico-syntactic graphs – Perform graph subsumption between graph-T and graph-H – Additive strategy • Not cascaded • Two approaches – Lexico-syntactic approach – Dependency-based approach • Results, Comparison, Conclusions

The Two Approaches • Lexico-syntactic approach – – – Lexical component Syntactic component Dependencies derived from phrase-based parse trees Negation thesaurus • Dependency-based approach – Dependencies from MINIPAR – Lexical component by default – Postprocessing (thanks to Vivi Nastase) • To eliminate unused information • To retain only dependencies among content words

Graph Subsumption • Map nodes and edges in H-graph to nodes and edges in T-graph • complex mapping based on – Named Entity Inferences: Overture Services Inc -- Overture – Word-level entailment / equivalence: take over – buy – Syntactic Info: • Yahoo is the agent of buying

From Sentences to Graph Representation • vertices represent content words • edges represent dependencies – local dependencies (intra-phrase) are straightforwardly obtained from a parse tree – remote dependencies are obtained using an extended functional tagger – Or from MINIPAR (for the second approach)

The Entailment Score • The score is so defined to be non-reflexive: entail(T, H) ≠ entail(H, T) Score is also used as confidence

The Parameters • the following parameters worked best on development α=. 5 β =. 5 γ=0

Negation • Explicit – Clue phrases • no, not, neither … nor • shortened forms: ‘nt • Implicit – Antonymy in Word. Net • Hypothetical sentences: “a possible visit by Clinton to China” does not entail “Clinton visited China” – a form of negation

Results – Lexico-Syntactic Approach System Accuracy Average precision Lexico-syntactic 0. 5900 0. 6047 Lex 0. 5663 0. 5823 Lex-cnt-words 0. 5875 0. 5725 Lex+syn 0. 5737 0. 5841 Lex+syn+neg 0. 5800 0. 6096 Lex+synt 0. 5813 0. 5941 lex+synt+neg 0. 5900 0. 6047

Comparison System Accuracy Average precision Lexico-syntactic 0. 5900 0. 6047 Lex+synt 0. 5813 0. 5941 Dependency-based 0. 5837 0. 5785

Conclusions • Lexical information significantly helps • The other components (synonymy, dependencies, negation) add value but not significantly

Missed Opportunities • Linguistic Level – Five = 5 – Tuscany province = province of Tuscany • Current subsumption algorithm is weak • T: Besancon is the capital of France’s watch and clockmaking industry and of high precision engineering. • H: Besancon is the capital of France. Solution: matching with more complex structures • World Knowledge

More Conclusions • Our system is light – Good for interactive environment such as Intelligent Tutoring Systems • No training involved – Just development to tune few parameters

One More Conclusion • It is not clear whethere is a difference among the two ways to obtain dependencies!

Two Related Lexico-Syntactic Approaches to Entailment Thank you everyone !