This paper describes our system of recognizing textual entailment for RTE-5 challenge at TAC 2009. We propose a textual entailment recognition framework and implement a system of classification which takes lexical, syntactic and semantic features as considered. To improve the performance, some lexical-semantic resources and web knowledge bases are also incorporated in the system. Official resul...

This paper presents a system that applies Textual Entailment recognition techniques to the AVE task. This is performed comparing representations of text snippets by means of a variety of lexical measures and syntactic structures. The representations of the question and the answer are compared determining if there is an entailment relation between them. The performed experiments over the English...

Our submission guesses at entailment based on word similarity between the hypotheses and the text. We attempt three kinds of comparisions: original words (with normalized dates and numbers) synonyms, and antonyms. Each of the three comparisions contributes a different weight to the entailment decision. Our results are insignificantly better than chance for the two-way comparison. However, for t...

This paper is about our approach to answer validation, which centered by a Recognizing Textual Entailment (RTE) core engine. We first combined the question and the answer into Hypothesis (H) and view the document as Text (T); then, we used our RTE system to check whether the entailment relation holds between them. Our system was evaluated on the Answer Validation Exercise (AVE) task and achieve...

In this paper, we introduce a new framework for recognizing textual entailment (RTE) which depends on extraction of the set of publicly-held beliefs – known as discourse commitments – that can be ascribed to the author of a text (t) or a hypothesis (h). We show that once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identi...

This paper reports the methods used by the EHIME team for textual entailment recognition in NTCIR-10, RITE-2. We participated in the Japanese BC subtask and Japanese MC subtask. We used Markov logic to infer textual entailment relations. In our Markov logic network, words and hyponyms are used as features.

In this paper we describe the SemKer system participating to the fifth Recognizing of Textual Entailment (RTE5) challenge. The major novelty with respect to the systems with which we participated to the previous challenges is the use of semantic knowledge based on Wikipedia. More specifically, we used it to enrich the similarity measure between pairs of text and hypothesis (i.e. the tree kernel...

the benefits of textual input enhancement in the acquisition of linguistic forms have produced mixed results in sla literature. the present study investigates the effects of textual enhancement on adult foreign language intake of two english linguistic forms-subjunctive mood and inversion structures-to explore the role of the type of linguistic items in input enhancement studies. it also invest...