Recognising Textual Entailment Focusing on Non-Entailing Text and Hypothesis (original) (raw)

TALP at TAC 2008: A Semantic Approach to Recognizing 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.

Recognizing textual entailment using a machine learning approach

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.

An approach using named entities for recognizing textual entailment

Proceedings of the Fourth PASCAL Challenges …, 2008

This paper describes the Sagan system in the context of the Fourth Pascal Recognizing Textual Entailment (RTE-4) Evaluation Challenge. Sagan applies a Support Vector Machine classifier to examples characterized by four features based on: edit distance, distance in WordNet and Longest Common Substring between text and hypothesis. Additionally, we created a filter applying hand-crafted rules based on Named Entities to detect cases where no entailment was found. Despite this simple approach, results are promising. Applying the Named Entity filter yields a small improvement in precision.

Fourth Recognising Textual Entailment

2012

Tac 2008 clear rte system report: Facet-based entailment

… of the Text Analysis Conference (TAC …, 2009

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 entailed. We also provide an error analysis based on the facets of hypotheses most likely to have led to their misclassification.

Experiments of UNED at the third recognising textual entailment challenge

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.

Recognizing Textual Entailment

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.

Recognizing Textual Entailment using Dependency Analysis and Machine Learning

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, 2015

This paper presents a machine learning system that uses dependency-based features and lexical features for recognizing textual entailment. The proposed system evaluates the feature values automatically. The performance of the proposed system is evaluated by conducting experiments on RTE1, RTE2 and RTE3 datasets. Further, a comparative study of the current system with other ML-based systems for RTE to check the performance of the proposed system is also presented. The dependency-based heuristics and lexical features from the current system have resulted in significant improvement in accuracy over existing state-of-art ML-based solutions for RTE.