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...

This report is about our participation in the Answer Validation Exercise (AVE) 2007. Our system utilizes a Recognizing Textual Entailment (RTE) system as a component to validate answers. We first change the question and the answer into Hypothesis (H) and view the document as Text (T), in order to cast the AVE task into a RTE problem. Then, we use our RTE system to tell us whether the entailment...

This paper reports on LCC’s participation at the Third PASCAL Recognizing Textual Entailment Challenge. First, we summarize our semantic logical-based approach which proved successful in the previous two challenges. Then we highlight this year’s innovations which contributed to an overall accuracy of 72.25% for the RTE 3 test data. The novelties include new resources, such as eXtended WordNet K...

This paper describes the textual entailment system of FLL for RITE-2 task in NTCIR-10. Our system is based on a set of local alignments conducted on different linguistic units, such as word, Japanese base phrase, numerical expression, Named Entity, and sentence. Our system uses features obtained from local alignments’ results. We applied our system to Japanese BC task and Japanese MC task at fo...

We envisage retrieval in textual case-based reasoning (TCBR) as an instance of abductive reasoning. The two main subtasks underlying abductive reasoning are ‘hypotheses generation’ where plausible case hypotheses are generated, and ‘hypothesis testing’ where the best hypothesis is selected among these in sequel. The central idea behind the presented two-stage retrieval model for TCBR is that re...