Abstract
A two-way textual entailment (TE) recognition system that uses lexical and syntactic features has been described in this paper. Th e h ybrid TE s ystem i s b ased on th e S upport V ector Machine that uses twenty three features for lexical similarity and the output tag from a rule based syntactic two-way TE system as another feature. The important lexical features that are used in the present system are: WordNet based unigram match, bigram match, longest c ommon s ub-sequence, s kip-gram, s temming, named entity matching and lexical distance. In the syntactic TE system, th e important fe atures u sed a re: s ubject-subject comparison, s ubject-verb com parison, object-verb c omparison and cross subject-verb comparison. The hybrid system has been developed using the collection of RTE-2 test annotated set, RTE-3 development set and RTE-3 test gold set that includes 2400 text-hypothesis p airs. Eval uation s cores ob tained on th e RTE-4 te st set (includes 1000 te xt-hypothesis pairs) show 55.30% precision and 58.40% recall for YES decisions and 55.93% precision and 52.80% recall for NO decisions.
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