tailieunhanh - Báo cáo khoa học: "A Modular Open-Source System for Recognizing Textual Entailment"

This paper introduces B IU T EE1 , an opensource system for recognizing textual entailment. Its main advantages are its ability to utilize various types of knowledge resources, and its extensibility by which new knowledge resources and inference components can be easily integrated. These abilities make B IU T EE an appealing RTE system for two research communities: (1) researchers of end applications, that can benefit from generic textual inference, and (2) RTE researchers, who can integrate their novel algorithms and knowledge resources into our system, saving the time and effort of developing a complete RTE system from scratch | BiuTee a Modular Open-Source System for Recognizing Textual Entailment Asher Stern Computer Science Department Bar-Ilan University Ramat-Gan 52900 Israel astern7@ Ido Dagan Computer Science Department Bar-Ilan University Ramat-Gan 52900 Israel dagan@ Abstract This paper introduces BiuTee1 an opensource system for recognizing textual entailment. Its main advantages are its ability to utilize various types of knowledge resources and its extensibility by which new knowledge resources and inference components can be easily integrated. These abilities make BiuTee an appealing RTE system for two research communities 1 researchers of end applications that can benefit from generic textual inference and 2 RTE researchers who can integrate their novel algorithms and knowledge resources into our system saving the time and effort of developing a complete RTE system from scratch. Notable assistance for these researchers is provided by a visual tracing tool by which researchers can refine and debug their knowledge resources and inference components. 1 Introduction Recognizing Textual Entailment RTE is the task of identifying given two text fragments whether one of them can be inferred from the other Dagan et al. 2006 . This task generalizes a common problem that arises in many tasks at the semantic level of NLP. For example in Information Extraction IE a system may be given a template with variables . X is employed by Y and has to find text fragments from which this template with variables replaced by proper entities can be inferred. In Summarization a good summary should be inferred from the nlp downloads biutee 73 given text and in addition should not contain duplicated information . sentences which can be inferred from other sentences in the summary. Detecting these inferences can be performed by an RTE system. Since first introduced several approaches have been proposed for this task ranging from shallow lexical similarity methods