We describe the system we used at the PASCAL-2005 Recognizing Textual Entailment Challenge. Our method for recognizing entailment is based on calculating “directed” sentence similarity: checking the directed “semantic” word overlap between the text and the hypothesis. We use frequency-based term weighting in combination with two different lexical similarity measures. Although one version of the...

In this paper we present the use of the AORTE system in recognizing textual entailment. AORTE allows the automatic acquisition and alignment of ontologies from text. The information resulted from aligning ontologies created from text fragments is used in classifying textual entailment. We further introduce the set of features used in classifying textual entailment. At the TAC RTE4 challenge the...

This document describes the participation of the SINAI Research Group in the 7 challenge on Recognition of Textual Entailment (RTE). Our approach extends the promising results obtained in the last campaing into a well know framework for textual entailment recognition known as EDITS. Although the proposed solution is modest, results encourage us in the use of Personalized Page Rank as a techniqu...

In this paper we present a novel similarity between pairs of co-indexed trees to automatically learn textual entailment classifiers. We defined a kernel function based on this similarity along with a more classical intra-pair similarity. Experiments show an improvement of 4.4 absolute percent points over state-of-the-art methods.

Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addres...

While there is a strong intuition that word alignments (e.g. synonymy, hyperonymy) play a relevant role in recognizing textto-text semantic inferences (e.g. textual entailment, semantic similarity), this intuition is often not reflected in the system performances and there is a general need of a deeper comprehension of the role of lexical resources. This paper provides an empirical analysis of ...

This paper presents a system that uses machine learning algorithms for the task of recognizing textual entailment in Spanish language. The datasets used include SPARTE Corpus and a translated version to Spanish of RTE3, RTE4 and RTE5 datasets. The features chosen quantify lexical, syntactic and semantic level matching between text and hypothesis sentences. We analyze how the different sizes of ...

This paper presents our approach to semantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two questions: (1) Can we solve these two subtasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To address them, we extracted seven types of features including text difference meas...

System validation subtask in NTCIR aims at developing techniques to deal with many kinds of language phenomena about textual entailment. This paper introduces our system participating in NTCIR-11 RITE-VAL SV Subtask. By adopting different combination of features related to WordNet, Tongyici Cilin, and syntactic information, 5 SV-BC and 5 SV-MC formal runs were submitted. The best BC run achieve...

In this paper we present a report of the two different runs submitted to the task 8 of Semeval 2012 for the evaluation of Cross-lingual Textual Entailment in the framework of Content Synchronization. Both approaches are based on textual similarity, and the entailment judgment (bidirectional, forward, backward or no entailment) is given based on a set of decision rules. The first approach uses t...