We compare two approaches to the problem of Textual Entailment: SLIM, a compositional approach modeling the task based on identifying relations in the entailment pair, and BoLI, a lexical matching algorithm. SLIM’s framework incorporates a range of resources that solve local entailment problems. A search-based inference procedure unifies these resources, permitting them to interact flexibly. Bo...

In this paper we present a novel approach for learning entailment relations from positive and negative examples. We define a similarity between two text-hypothesis pairs based on a syntactic and lexical information. We experimented our model within the RTE 2006 challenge obtaining the accuracy of 63.88% and 62.50% for the two submissions.

This study aimed to explore the function and frequency of textual metadiscourse markers (MDMs) in scientific English and Persian texts. Based on the qualitative and quantitative analysis of textual markers characterizing the selected genre, four different textbooks, two written in English and two in Persian were analyzed to identify the textual metadiscourse categories (including logical marker...

the present study attempted the relative effect of explicit teaching of textual metadiscourse markers on esap reading comprehension performance of iranian university students through an awareness raising experiment. a sixty-item multiple choice esap reading comprehension test of accounting was developed and validated to act as the pre-test and post-test. the test included items for assessing sp...

Text categorization has been a popular research topic for years and has become more or less a practical technology. However, there exists little research on math topic classification. Math documents contain both textual data and math expressions. The text and math can be considered as two related but different views of a math document. The goal of online math topic classification is to automati...

Textual entailment among sentences is an important part of applied semantic inference. In this paper we propose a novel technique to address the recognizing textual entailment challenge, which based on the distribution hypothesis that words that tend to occur in the same contexts tend to have similar meanings. Using the IDF of the overlapping words between the two propositions, we calculate the...

This paper proposes a general probabilistic setting that formalizes a probabilistic notion of textual entailment. We further describe a particular preliminary model for lexical-level entailment, based on document cooccurrence probabilities, which follows the general setting. The model was evaluated on two application independent datasets, suggesting the relevance of such probabilistic approache...

This paper describes a predominantly shallow approach to the rte-4 Challenge. We focus our attention on the non-entailing Text and Hypothesis pairs in the dataset. The system uses a Maximum Entropy framework to classify each pair of Text and Hypothesis as either yes or no, using a range of different feature sets based on an analysis of the existing non-entailing pairs in rte training data.

This paper describes the experiments developed and the results obtained in the participation of UNED in the Fourth Recognising Textual Entailment (RTE) Challenge. This year we decided to change the scope of our work with the aim of beginning to develop a system that performs a deeper analysis than the techniques used in the last editions. This participation has been the first step in the develo...

In order for a text to entail a hypothesis, the text usually must mention all of the information in the hypothesis. We use this observation as a basis for a simple system for detecting non-entailment. Unlike many previous lexically-based systems, we do not measure the degree of overlap or similarity, and we do no machine learning. This simple system performs well on the Recognizing Textual Enta...