ABSTRACT This paper describes our work in NTCIR-9 on RITE Binary-class (BC) subtask and Multi-class (MC) subtask in Simplified Chinese. We use classification method and SVM classifier to identify the textual entailment. We totally use thirteen statistical features as the classification features in our system. The system includes three parts: (1) Preprocessing, (2) Feature Extraction, (3) SVM Cl...

This paper reports on our Recognizing Textual Entailment (RTE) system developed for participation in the Text Analysis Conference RTE 2009 competition. The development of the system is based on the lexical entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize Wor...

In this paper, we describe the IMTKU (Information Management at TamKang University) textual entailment system for recognizing inference in text at NTCIR-9 RITE (Recognizing Inference in Text). We proposed a textual entailment system using a hybrid approach that integrate knowledge based and machine learning techniques for recognizing inference in text at NTCIR-9 RITE task. We submitted 3 offici...

We propose in this paper a greedy method to the problem of measuring semantic similarity between short texts. Our method is based on the principle of compositionality which states that the overall meaning of a sentence can be captured by summing up the meaning of its parts, i.e. the meanings of words in our case. Based on this principle, we extend wordto-word semantic similarity metrics to quan...

In this paper, we introduce a new framework for recognizing textual entailment which depends on extraction of the set of publiclyheld beliefs – known as discourse commitments – that can be ascribed to the author of a text or a hypothesis. Once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments ...

We approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts need to be explicitly stated. However, recognising subsentential semantic relations is not trivial. We address this problem by monitoring the proof of the theorem and detecting unpr...

In this paper, we briefly describe two enhancements of the cross-pair similarity model for learning textual entailment rules: 1) the typed anchors and 2) a faster computation of the similarity. We will report and comment on the preliminary experiments and on the submission results.

We examine adjective-noun (AN) composition in the task of recognizing textual entailment (RTE). We analyze behavior of ANs in large corpora and show that, despite conventional wisdom, adjectives do not always restrict the denotation of the nouns they modify. We use natural logic to characterize the variety of entailment relations that can result from AN composition. Predicting these relations d...

This paper focuses on the central role played by lexical information in the task of Recognizing Textual Entailment. In particular, the usefulness of lexical knowledge extracted from several widely used static resources, represented in the form of entailment rules, is compared with a method to extract lexical information from Wikipedia as a dynamic knowledge resource. The proposed acquisition me...

Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on formal semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similari...