In this paper we present a classifier for Recognising Textual Entailment (RTE) and Semantic Equivalence. We evaluate the performance of this classifier using an evaluation framework provided by the PASCAL RTE Challenge Workshop. Sentence–pairs are represented as a set of features, which are used by our decision tree classifier to determine if an entailment relationship exisits between each sent...

We present two regression models for the prediction of pairwise preference judgments among MT hypotheses. Both models are based on feature sets that are motivated by textual entailment and incorporate lexical similarity as well as local syntactic features and specific semantic phenomena. One model predicts absolute scores; the other one direct pairwise judgments. We find that both models are co...

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.

Novice academic writers, particularly Iranian graduate students (IGSs), upon entering an academic community, are hypothesized to face probable difficulties in practicing rhetorical expectations set by the experienced (EXP) members, hence, not being able to write in a way acceptable to these professionals. To explore the probable rhetorical distance between them, this study investigated the empl...

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...