Global software developments intensify parallel changes. Although parallel changes can improve performance, their interferences contribute to faults. Current Software Configuration Management (SCM) systems can detect the interference between changes at textual level. However, our empirical study shows that, compared with textual interference, semantic approach is more effective and efficient in...

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...

The Recognizing Textual Entailment System shown here is based on the use of a broad-coverage parser to extract dependency relationships; in addition, WordNet relations are used to recognize entailment at the lexical level. The work investigates whether the mapping of dependency trees from text and hypothesis give better evidence of entailment than the matching of plain text alone. While the use...

While a significant amount of research has been devoted to textual entailment, automated entailment from conversational scripts has received less attention. To address this limitation, this paper investigates the problem of conversation entailment: automated inference of hypotheses from conversation scripts. We examine two levels of semantic representations: a basic representation based on synt...

This paper presents how text summarization can be influenced by textual entailment. We show that if we use textual entailment recognition together with text summarization approach, we achieve good results for final summaries, obtaining an improvement of 6.78% with respect to the summarization approach only. We also compare the performance of this combined approach to two baselines (the one prov...

This paper addresses syntax-based paraphrasing methods for Recognizing Textual Entailment (RTE). In particular, we describe a dependency-based paraphrasing algorithm, using the DIRT data set, and its application in the context of a straightforward RTE system based on aligning dependency trees. We find a small positive effect of dependency-based paraphrasing on both the RTE3 development and test...

Work on the semantics of questions has argued that the relation between a question and its answer(s) can be cast in terms of logical entailment. In this paper, we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering (Q/A) systems. In our experiments, we show that when textual entail...

We challenge the NLP community to participate in a large-scale, distributed effort to design and build resources for developing and evaluating solutions to new and existing NLP tasks in the context of Recognizing Textual Entailment. We argue that the single global label with which RTE examples are annotated is insufficient to effectively evaluate RTE system performance; to promote research on s...

We introduce a new formal semantic model for annotating textual entailments, that describes restrictive, intersective and appositive modification. The model contains a formally defined interpreted lexicon, which specifies the inventory of symbols and the supported semantic operators, and an informally defined annotation scheme that instructs annotators in which way to bind words and constructio...