The Stanford Dependencies are a deep syntactic representation that are widely used for semantic tasks, like Recognizing Textual Entailment. But do they capture all of the semantic information a meaning representation ought to convey? This paper explores this question by investigating the feasibility of mapping Stanford dependency parses to Hobbsian Logical Form, a practical, event-theoretic sem...

This paper describes our system of recognizing textual entailment for RITE Traditional and Simplified Chinese subtasks at NTCIR10. We build a textual entailment recognition framework and implement a system that employs features of three categories, including string, structure and linguistic features, for the recognition. In addition, an entailment transformation approach is leveraged to align t...

The present study is an attempt to examine the psychological processes of noticing, retrieval, and generation their possible contribution process vocabulary learning retention among intermediate students. research method was experimental. One hundred twenty students were randomly assigned into four groups, namely Noticing through Input enhancement (n=30), Enhancement plus Input-Based Reviewing ...

assessment is considered as one of the fundamental elements in the field of foreign language acquisition. in order for communication take place, adequate number of vocabulary is needed to be known by the learners. the salient role of vocabulary in the field of foreign language acquisition resulted in the publication of several hundreds of papers and dozens of books. due to the dominant role of ...

In 2008 the Recognizing Textual Entailment Challenge (RTE-4) was proposed for the first time as a track at the Text Analysis Conference (TAC). Another important innovation introduced in this campaign was a three-judgment task, which required the systems to make a further distinction between pairs where the entailment does not hold because the content of H is contradicted by the content of T, an...

After defining what is understood by textual entailment and semantic equivalence, the present state and the desirable future of the systems aimed at recognizing them is shown. A compilation of the currently implemented techniques in the main Recognizing Textual Entailment and Semantic Equivalence systems is given.

There are fewer resources for textual entailment (TE) for Arabic than for other languages, and the manpower for constructing such a resource is hard to come by. We describe here a semi-automatic technique for creating a first dataset for TE systems for Arabic using an extension of the ‘headline-lead paragraph’ technique. We also sketch the difficulties inherent in volunteer annotators-based jud...