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D. Freitag, A. McCallum and F. Pereira, “Maximum En-tropy Markov Models for Information Extraction and Segmentation,” Proceedings of the Seventeenth Interna-tional Conference on Machine Learning, Morgan Kauf-mann, San Francisco, 2000, pp. 591-598.

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• TITLE: Chunk Parsing and Entity Relation Extracting to Chinese Text by Using Conditional Random Fields Model

  AUTHORS: Junhua Wu, Longxia Liu

  KEYWORDS: Information Extraction, Chunk Parsing, Entity Relation Extraction

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.2 No.3, September 8, 2010

  ABSTRACT: Currently, large amounts of information exist in Web sites and various digital media. Most of them are in natural lan-guage. They are easy to be browsed, but difficult to be understood by computer. Chunk parsing and entity relation extracting is important work to understanding information semantic in natural language processing. Chunk analysis is a shallow parsing method, and entity relation extraction is used in establishing relationship between entities. Because full syntax parsing is complexity in Chinese text understanding, many researchers is more interesting in chunk analysis and relation extraction. Conditional random fields (CRFs) model is the valid probabilistic model to segment and label sequence data. This paper models chunk and entity relation problems in Chinese text. By transforming them into label solution we can use CRFs to realize the chunk analysis and entities relation extraction.