tailieunhanh - Báo cáo khoa học: "An Open-Source Package for Recognizing Textual Entailment"

This paper presents a general-purpose open source package for recognizing Textual Entailment. The system implements a collection of algorithms, providing a configurable framework to quickly set up a working environment to experiment with the RTE task. Fast prototyping of new solutions is also allowed by the possibility to extend its modular architecture. We present the tool as a useful resource to approach the Textual Entailment problem, as an instrument for didactic purposes, and as an opportunity to create a collaborative environment to promote research in the field. . | An Open-Source Package for Recognizing Textual Entailment Milen Kouylekov and Matteo Negri FBK - Fondazione Bruno Kessler Via Sommarive 18 38100 Povo TN Italy kouylekov negri @ Abstract This paper presents a general-purpose open source package for recognizing Textual Entailment. The system implements a collection of algorithms providing a configurable framework to quickly set up a working environment to experiment with the RTE task. Fast prototyping of new solutions is also allowed by the possibility to extend its modular architecture. We present the tool as a useful resource to approach the Textual Entailment problem as an instrument for didactic purposes and as an opportunity to create a collaborative environment to promote research in the field. 1 Introduction Textual Entailment TE has been proposed as a unifying generic framework for modeling language variability and semantic inference in different Natural Language Processing NLP tasks. The Recognizing Textual Entailment RTE task Dagan and Glickman 2007 consists in deciding given two text fragments respectively called Text - T and Hypothesis - H whether the meaning of H can be inferred from the meaning of T as in T Yahoo acquired Overture H Yahoo owns Overture The RTE problem is relevant for many different areas of text processing research since it represents the core of the semantic-oriented inferences involved in a variety of practical NLP applications including Question Answering Information Retrieval Information Extraction Document Summarization and Machine Translation. However in spite of the great potential of integrating RTE into complex NLP architectures little has been done to actually move from the controlled scenario pro posed by the RTE evaluation campaigns1 to more practical applications. On one side current RTE technology might not be mature enough to provide reliable components for such integration. Due to the intrinsic complexity of the problem in fact state of the art results still show .