In order for a text to entail a hypothesis, the text usually must mention all of the information in the hypothesis. We use this observation as a basis for a simple system for detecting non-entailment. Unlike many previous lexically-based systems, we do not measure the degree of overlap or similarity, and we do no machine learning. This simple system performs well on the Recognizing Textual Enta...

In this paper, we describe a method for assessing student answers, modeled as a paraphrase identification problem, based on substitution by Basic English variants. Basic English paraphrases are acquired from the Simple English Wiktionary. Substitutions are applied both on reference answers and student answers in order to reduce the diversity of their vocabulary and map them to a common vocabula...

This paper presents CELI’s participation in the SemEval The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge (Task7) and Cross-lingual Textual Entailment for Content Synchronization task (Task 8).

This paper describes the Cambridge submission to the SemEval-2010 Parser Evaluation using Textual Entailment (PETE) task. We used a simple definition of entailment, parsing both T and H with the C&C parser and checking whether the core grammatical relations (subject and object) produced for H were a subset of those for T. This simple system achieved the top score for the task out of those syste...

This paper explores how a battery of unsupervised techniques can be used in order to create large, high-quality corpora for textual inference applications, such as systems for recognizing textual entailment (TE) and textual contradiction (TC). We show that it is possible to automatically generate sets of positive and negative instances of textual entailment and contradiction from textual corpor...

In this work we investigate methods to enable the detection of a specific type of textual entailment (strict entailment), starting from the preliminary assumption that these relations are often clearly expressed in texts. Our method is a statistical approach based on what we call textual entailment patterns, prototypical sentences hiding entailment relations among two activities. We experimente...

This paper overviews BUPTTeam’s participation in the main task organized within the RTE7 Evaluation. In this paper we propose a method to calculate the similarity between text and hypothesis based on the TF/IDF values. Our system designed to recognize textual entailment typically employ lexical information. The evaluation results show that our method is effective for RTE task.

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...