For textual entailment recognition systems, it is often important to correctly handle Generalized quantifiers (GQ). In this paper, we explore ways of encoding GQs in a recent framework of Dependency-based Compositional Semantics, especially aiming to correctly handle linguistic knowledge like hyponymy when GQs are involved. We use both the selection operator mechanism and a new relation extensi...

This paper describes the CLEAR team’s submission to the 2008 Text Analysis Conference under the Recognizing Textual Entailment track. The system breaks text fragments down into fine-grained semantic facets and performs entailment recognition on these. We show that, in the relevant subset of the data, we can achieve 90% accuracy in pinpointing the specific facet of a hypothesis that is not entai...

The Recognizing Textual Entailment System shown here is based on the use of a broad-coverage parser to extract dependency relationships; in addition, WordNet relations are used to recognize entailment at the lexical level. The work investigates whether the mapping of dependency trees from text and hypothesis give better evidence of entailment than the matching of plain text alone. While the use...

In this paper, we explore the application of inference rules for recognizing textual entailment (RTE). We start with an automatically acquired collection and then propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based representation from texts, which aims to provide a proper base for the inference rule application. ...

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...

This paper studies the effect of semantic knowledge expansion applied to the Textual Entailment Recognition task. In comparison to the already existing approaches we introduce a new set of similarity measures that captures hidden semantic relations among different syntactic categories in a sentence. The focus of our study is also centred on the synonym, antonym and verb entailment expansion of ...

In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of 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 WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesi...