Textual Entailment Recognition (TER), also known as natural language inference, is a
crucial task in natural language processing that combines many fundamental aspects of …

Semantic representation reflects the meaning of the text as it may be understood by humans.
Thus, it contributes to facilitating various automated language processing applications …

Natural Language Inference (NLI) is a hot topic research in natural language processing,
contradiction detection between sentences is a special case of NLI. This is considered a …

Determining the textual entailment between texts is important in many NLP tasks, such as
summarization, question answering, and information extraction and retrieval. Various …

Textual Entailment is one of the recent natural language processing challenges, where the
meaning of an expression “Hypothesis” could be entailed by the meaning of another …

Abstract Recognizing Textual Entailment (RTE) is a task of Natural language processing
(NLP) in which two text fragments denoted Text (T) and Hypothesis (H) are processed by a …

This paper presents the system submitted by University of Wolverhampton for SemEval-
2014 task 1. We proposed a machine learning approach which is based on features …

This thesis explores a range of approaches to the task of recognising textual entailment
(RTE), ie determining whether one text snippet entails another, for Arabic, where we are …

This paper presents a textual entailment (TE) model that considers entailment as an
optimization problem. The proposed TE model employs a genetic algorithm to derive an …

Recognizing the relation of entailment between sentences is an important and common part
of linguistic communication. The recognizing textual entailment task has been proposed as a …