This paper describes the TU system that participated in the Entrance Exam Subtask of NTCIR-9 RITE. The system consists of two phases: alignment and entailment relation recognition. In the alignment phase, the system aligns words in the two sentences by exploiting diverse lexical resources such as entailment information, hypernym-hyponym relations and synonyms. Based on the alignments and relati...

This paper describes our participation in the task denominated Cross-Lingual Textual Entailment (CLTE) for content synchronization. We represent an approach to CLTE using machine translation to tackle the problem of multilinguality. Our system resides on machine learning and in the use of WordNet as semantic source knowledge. Results are very promising always achieving results above mean score.

Implicative verbs (e.g. manage) entail their complement clauses, while non-implicative verbs (e.g. want) do not. For example, while managing to solve the problem entails solving the problem, no such inference follows from wanting to solve the problem. Differentiating between implicative and non-implicative verbs is therefore an essential component of natural language understanding, relevant to ...

The BnO team participated in the Recognizing Inference in TExt (RITE) subtask of the NTCIR-10 Workshop [5]. This paper describes our textual entailment recognition system with experimental results for the five Japanes subtasks: BC, MC, EXAMBC, EXAM-SEARCH, and UnitTest. Our appoach includes a shallow method based on word overlap features and named entity recognition; and a novel inferencebased ...

In many questions in Community Question Answering sites users look for the advice or opinion of other users who might offer diverse perspectives on a topic at hand. The novel task we address is providing supportive evidence for human answers to such questions, which will potentially help the asker in choosing answers that fit her needs. We present a support retrieval model that ranks sentences ...

In this paper we define a measure for textual entailment recognition based on the graph matching theory applied to syntactic graphs. We describe the experiments carried out to estimate measure’s parameters with SVM and we report the results obtained on the Textual Entailment Challenge development and testing set.

This paper describes our system of recognizing textual entailment for RITE Chinese subtask at NTCIR-9. We build a textual entailment recognition framework and implement a system that employs string, syntactic, semantic and some specific features for the recognition. To improve the system’s performance, a twostage recognition strategy is utilized, which first judge entailment or no entailment, a...

This paper presents a novel method for recognizing textual entailment which derives the hypothesis from the text through a sequence of parse tree transformations. Unlike related approaches based on tree-edit-distance, we employ transformations which better capture linguistic structures of entailment. This is achieved by (a) extending an earlier deterministic knowledge-based algorithm with synta...

We demonstrate a simple and easy-to-use system to produce logical semantic representations of sentences. Our software operates by composing semantic formulas bottom-up given a CCG parse tree. It uses flexible semantic templates to specify semantic patterns. Templates for English and Japanese accompany our software, and they are easy to understand, use and extend to cover other linguistic phenom...

We introduce a system for textual entailment that is based on a probabilistic model of entailment. The model is defined using a calculus of transformations on dependency trees, which is characterized by the fact that derivations in that calculus preserve the truth only with a certain probability. The calculus is successfully evaluated on the datasets of the PASCAL Challenge on Recognizing Textu...