Fourth Recognising Textual Entailment

2008

This paper describes our experiments on Textual Entailment in the context of the Fourth Recognising Textual Entailment (RTE-4) Evaluation Challenge at TAC 2008 contest. Our system uses a Machine Learning approach with AdaBoost to deal with the RTE challenge. We perform a lexical, syntactic, and semantic analysis of the entailment pairs. From this information we compute a set of semantic-based distances between sentences. We improved our baseline system for the RTE-3 challenge with more Language Processing techniques, an hypothesis classifier, and new semantic features. The results show no general improvement with respect to the baseline.

Machine Learning Challenges. …, 2006

This paper describes the PASCAL Network of Excellence Recognising Textual Entailment (RTE) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This applicationindependent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.

2010

We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset.

Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing - RTE '07, 2007

This paper presents the Third PASCAL Recognising Textual Entailment Challenge (RTE-3), providing an overview of the dataset creating methodology and the submitted systems. In creating this year's dataset, a number of longer texts were introduced to make the challenge more oriented to realistic scenarios. Additionally, a pool of resources was offered so that the participants could share common tools. A pilot task was also set up, aimed at differentiating unknown entailments from identified contradictions and providing justifications for overall system decisions. 26 participants submitted 44 runs, using different approaches and generally presenting new entailment models and achieving higher scores than in the previous challenges.

2007

This paper discusses the recognition of textual entailment in a text-hypothesis pair by applying a wide variety of lexical measures. We consider that the entailment phenomenon can be tackled from three general levels: lexical, syntactic and semantic. The main goals of this research are to deal with this phenomenon from a lexical point of view, and achieve high results considering only such kind of knowledge. To accomplish this, the information provided by the lexical measures is used as a set of features for a Support Vector Machine which will decide if the entailment relation is produced. A study of the most relevant features and a comparison with the best state-of-the-art textual entailment systems is exposed throughout the paper. Finally, the system has been evaluated using the Second PASCAL Recognising Textual Entailment Challenge data and evaluation methodology, obtaining an accuracy rate of 61.88%.

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.

2011

This paper describes the Recognizing Textual Entailment (RTE) system that our teams developed for TAC 2011. Our system combines the entailment score calculated by lexicallevel matching with the machine-learningbased filtering mechanism using various features obtained from lexical-level, chunk-level and predicate argument structure-level information. In the filtering mechanism, we try to discard the T-H pairs that have high entailment score and are actually not entailment. That is, for filtering false positive T-H pairs caused by our lexical-level manner, we use additional information like features from word chunks and predicate-argument structures.

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.

2015

Abstract. This paper discusses the recognition of textual entailment in a text-hypothesis pair by applying a wide variety of lexical measures. We consider that the entailment phenomenon can be tackled from three general levels: lexical, syntactic and semantic. The main goals of this research are to deal with this phenomenon from a lexical point of view, and achieve high results considering only such kind of knowledge. To accomplish this, the information provided by the lexical measures is used as a set of features for a Support Vector Machine which will decide if the entailment relation is produced. A study of the most relevant features and a comparison with the best state-of-the-art textual entailment systems is exposed throughout the paper. Finally, the system has been evaluated using the Second PASCAL Recognising Textual Entailment Challenge data and evaluation methodology, obtaining an accuracy rate of 61.88%. 1

Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing - RTE '07, 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.