tailieunhanh - Báo cáo khoa học: "Extracting Relations with Integrated Information Using Kernel Methods"

Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. . | Extracting Relations with Integrated Information Using Kernel Methods Shubin Zhao Ralph Grishman Department of Computer Science New York University 715 Broadway 7th Floor New York NY 10003 shubinz@ grishman@ Abstract Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered tokenization sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task using Support Vector Machines and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels. 1 Introduction Information extraction subsumes a broad range of tasks including the extraction of entities relations and events from various text sources such as newswire documents and broadcast transcripts. One such task relation detection finds instances of predefined relations between pairs of entities such as a Located-In relation between the entities Centre College and Danville KY in the phrase Centre College in Danville KY. The entities are the individuals of selected semantic types such as people organizations countries . which are referred to in the text. Prior approaches to this task Miller et al. 2000 Zelenko et al. 2003 have relied on partial or full syntactic analysis. Syntactic analysis can find relations not readily identified based on .