In this paper we report the results obtained in the Semantic Textual Similarity (STS) task, with a system primarily developed for textual entailment. Our results are quite promising, getting a run ranked 39 in the official results with overall Pearson, and ranking 29 with the Mean metric.

Discourse references, notably coreference and bridging, play an important role in many text understanding applications, but their impact on textual entailment is yet to be systematically understood. On the basis of an in-depth analysis of entailment instances, we argue that discourse references have the potential of substantially improving textual entailment recognition, and identify a number o...

In this paper we propose a general method for the combination of specialized textual entailment engines. Each engine is supposed to address a specific language phenomenon, which is considered relevant for drawing semantic inferences. The model is based on the idea that the distance between the Text and the Hypothesis can be conveniently decomposed into a combination of distances estimated by si...

This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year’s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output...

This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2).1 We describe the RTE2 dataset and overview the submissions for the challenge. One of the main goals for this year’s dataset was to provide more “realistic” text-hypothesis examples, based mostly on outputs of actual systems. The 23 submissions for the challenge present diverse approaches and research direct...

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and...

The aim of the current study is to propose a system, which can automatically deduce entailment relations of textual pairs. The system mainly uses seven features and a decision tree is utilized as a prediction model of the system and seven features of textual pairs are employed to be input of the prediction model. The experimental results for dataset Formal-run based on our proposed method are e...

This paper presents CELI’s participation in the SemEval Cross-lingual Textual Entailment for Content Synchronization task.

This year, besides providing support to other groups participating in cross-language Question Answering (QA) tasks, and submitting runs both for the monolingual Italian and the cross-language Italian/English tasks, the ITC-irst participation in the CLEF campaign concentrated on the Answer Validation Exercise (AVE). The participation in the AVE task, with an answer validation module based on tex...

Manuscript Number: Title: Automatic Event-Level Textual Emotion Sensing Using Mutual Action Histogram between Entities Article Type: Full Length Article