Towards a Taxonomy of Textual Entailments ∗

2007

Recognising Textual Entailment (RTE) was proposed in 2005 as a generic task, aimed at building systems capable of capturing the semantic variability of texts and performing natural language inferences. These systems can be integrated in NLP applications, improving their performance in fine-grained semantic analysis.

Journal of Research and …, 2009

This paper presents three methods for solving the problem of textual entailment, obtained from an equal number of text-to-text similarity metrics. The first method starts with the directional measure of text-to-text similarity presented in , and integrates word sense disambiguation and several heuristics. The second method exploits the relations between the cosine directional measures of similarity as means to identify textual entailment. Finally, the third method relies on the directional variant of Levenshtein distance between two words. Each "word" in this method is a string consisting of all the words concatenated. In all these methods the decision about an entailment relation depends on the relation established between these measures of similarity. The methods are applied and evaluated on the whole set of text-hypothesis pairs included in the PASCAL RTE-1 development dataset (RTE-1, 2005). The corresponding accuracy and statistics are presented for each method.

Proceedings of the ACL- …, 2007

Recognizing and generating textual entailment and paraphrases are regarded as important technologies in a broad range of NLP applications, including, information extraction, summarization, question answering, information retrieval, machine translation and text generation. Both textual entailment and paraphrasing address relevant aspects of natural language semantics. Entailment is a directional relation between two expressions in which one of them implies the other, whereas paraphrase is a relation in which two expressions convey essentially the same meaning. Indeed, paraphrase can be defined as bi-directional entailment. While it may be debatable how such semantic definitions can be made well-founded, in practice we have already seen evidence that such knowledge is essential for many applications.

Since 2005, researchers have worked on a broad task called Recognizing Textual Entailment (RTE), which is designed to focus efforts on general textual inference capabilities, but without constraining participants to use a specific representation or reasoning approach. There have been promising developments in this sub-field of Natural Language Processing (NLP), with systems showing steady improvement, and investigations of a range of approaches to the problem.

… Workshop on Textual Entailment …, 2007

Recognizing and generating textual entailment and paraphrases are regarded as important technologies in a broad range of NLP applications, including, information extraction, summarization, question answering, information retrieval, machine translation and text generation. Both textual entailment and paraphrasing address relevant aspects of natural language semantics. Entailment is a directional relation between two expressions in which one of them implies the other, whereas paraphrase is a relation in which two expressions convey essentially the same meaning. Indeed, paraphrase can be defined as bi-directional entailment. While it may be debatable how such semantic definitions can be made well-founded, in practice we have already seen evidence that such knowledge is essential for many applications.

2010

2005

2009

In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application. The evaluation of our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible improvements.

2009

Abstract The goal of identifying textual entailment–whether one piece of text can be plausibly inferred from another–has emerged in recent years as a generic core problem in natural language understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings.

2005

In this paper we define two intermediate models of textual entailment, which correspond to lexical and lexical-syntactic levels of representation. We manually annotated a sample from the RTE dataset according to each model, compared the outcome for the two models, and explored how well they approximate the notion of entailment. We show that the lexicalsyntactic model outperforms the lexical model, mainly due to a much lower rate of false-positives, but both models fail to achieve high recall. Our analysis also shows that paraphrases stand out as a dominant contributor to the entailment task. We suggest that our models and annotation methods can serve as an evaluation scheme for entailment at these levels.