This paper presents the second round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2013. The task was designed to promote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Participants were presented with datasets for different language pairs, where multi-direc...

This paper proposes a methodology for generating specialized Japanese data sets for textual entailment, which consists of pairs decomposed into basic sentence relations. We experimented with our methodology over a number of pairs taken from the RITE-2 data set. We compared our methodology with existing studies in terms of agreement, frequencies and times, and we evaluated its validity by invest...

In this paper, we first compare several strategies to handle the newly proposed three-way Recognizing Textual Entailment (RTE) task. Then we define a new measurement for a pair of texts, called Textual Relatedness, which is a weaker concept than semantic similarity or paraphrase. We show that an alignment model based on the predicate-argument structures using this measurement can help an RTE sy...

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.

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...