Information verification improvement by textual entailment methods

Semantic Methods for Textual Entailment

The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, a new approach is taken to apply world knowledge in much the same way as humans, but captured in large semantic graphs such as WordNet. We show that semantic graphs made of synsets and selected relationships between them enable fairly simple methods that pr...

Recognizing Textual Entailment with Statistical Methods

In this paper we propose a new cause-effect non-symmetric measure applied to the task of Recognizing Textual Entailment .First we searched over a big corpus for sentences which contains the discourse marker “because” and collected cause-effect pairs. The entailment recognition is based on measure the cause-effect relation between the text and the hypothesis using the relative frequencies of wor...

Modeling textual entailment with role semantic information

In this thesis, we present a novel approach for modeling textual entailment using lexicalsemantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution ...

Textual Entailment

Textual Entailment

The task of Recognizing Textual Entailment (RTE) requires automated systems to identify when two spans of text share a common meaning -for example, that ``Alphaville Inc.'s attempted acquisition of Bauhaus led to a jump in both companies' stock prices'' entails ``Bauahaus' stock rose'', but not ``Alphaville acquired Bauhaus''. This general capability would be a solid proxy for Natural Language ...