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...

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...

This paper provides a brief description of the system for recognizing textual entailment (RTE) Language Computer Corporation (LCC) used in the 2008 TAC RTE-4 Evaluation. Our RTE-4 work follows our previous work (Hickl and Bensley, 2007; Bensley and Hickl, 2008; Hickl, 2008) in using a pipeline of lightweight, largely statistical systems for commitment extraction, lexical alignment, and entailme...

This paper covers the recognition of textual entailment by means of different approaches based on lexical similarities and syntactic trees. These approaches are easily portable to other languages. We present the achieved results for each individual approach and we propose a simple voting strategy between these approaches and our previous system (presented in Second PASCAL Recognising Textual En...

Systems designed to recognize textual entailment typically employ both syntax and semantics. Our goal in this paper is to explore the degree to which semantics alone can be used to accurately detect entailment so that we can gain a better understanding of this single component within an entailment system. This paper reports the knowledge-bases considered and selected for person names, locations...

Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual ...