Different Approaches For Modeling Textual Entailment

2026 words - 8 pages

Different approaches for modeling textual entailment have been suggested in the literature. These approaches ranging from shallow approaches like measuring lexical overlap to syntactic parsing and the WordNet relations’ utilization (Bar-Haim et al., 2006b). Most of these approaches apply lexical matching. Some of the approaches represent the text snippets (T and H) as dependency (or syntactic) tree before apply an actual task. Other systems, for solving the T-H entailment problems use the semantic relation such as logical inference. Lately, there has been an activity with respect to more structured meaning representations, abstracting away from the semantically irrelevant surface.
A bag of words (BoW) method is presented by (Glickman, Dagan, & Koppel, 2005), which depend on lexical entailment. This system is comparatively simple, because it does not depend on syntactic or other deeper analysis. Glickman et al. assume that the content word meanings in a H= {u1, …, um} can be assigned truth values. Furthermore, if all lexical components of H are true then H is assumed to be true. As estimating the probability of entailment, they presume that the truth probability of a term in H is independent of the truth of the other terms in H. This corresponds to expect that each word in H will not be entailed from the cumulative context of T as a whole, but from a specific word in T. The authors apply unsupervised empirical estimation of the lexical entailment probabilities depend on word co-occurrence frequencies from the web to find the T-H pairs similarity. Finally, an entailment hold is decided if the estimated entailment probability exceeding a threshold, which empirically tuned, or not.
Adams’s system (Adams, 2006), which based primarily on the notion of lexical overlapped, is developed to the (Glickman, et al., 2005)’s approach. The sentence is treated as BoW in this system. Therefore, the number of overlapping words between a pair (T-H) could be regarded as features. The system starts with a similarity of BoW overlap measure, derived from a combination of WordNet lexical chains to the lexical entailment probability as in (Glickman, et al., 2005). Then, it looks for negations not found in the mapping, and for the lexical edit distance of the mapping. These items are later entered into a decision tree to find the overall entailment.
Jijkoun and de Rijke (Jijkoun & de Rijke, 2005) describe a similar method. For every (T-H) pair, each sentence considered as the bag of words and computes directed sentence similarity score. To check for entailment, the score is measured against a threshold. Primarily, for every word in H, the most similar word in T is found according to the word similarity measure (Lin, 1998c). If such a similar word exists, the weighted similarity value is added to the score of total similarity. Otherwise, the weight of the word (using idf ) is subtracted, penalizing words in H matching words in T.
The BLEU (Bilingual Evaluation...

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