首页> 外文会议>Computer Processing of Oriental Languages: Beyond the Orient: The Research Challenges Ahead; Lecture Notes in Artificial Intelligence; 4285 >Identification of Maximal-Length Noun Phrases Based on Expanded Chunks and Classified Punctuations in Chinese
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Identification of Maximal-Length Noun Phrases Based on Expanded Chunks and Classified Punctuations in Chinese

机译:基于扩展块和汉语标点符号的最大长度名词短语识别

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摘要

In general, there are two types of noun phrases (NP): Base Noun Phrase (BNP), and Maximal-Length Noun Phrase (MNP). MNP identification can largely reduce the complexity of full parsing, help analyze the general structure of complex sentences, and provide important clues for detecting main predicates in Chinese sentences. In this paper, we propose a 2-phase hybrid approach for MNP identification which adopts salient features such as expanded chunks and classified punctuations to improve performance. Experimental result shows a high quality performance of 89.66% in F_1-measure.
机译:通常,名词短语(NP)有两种类型:基本名词短语(BNP)和最大长度名词短语(MNP)。 MNP识别可以大大降低完整解析的复杂度,有助于分析复杂句子的总体结构,并为检测汉语句子中的主要谓词提供重要线索。在本文中,我们提出了一种用于MNP识别的2相混合方法,该方法采用了显着特征(例如扩展的块和分类的标点符号)来提高性能。实验结果表明,F_1量度的高质量性能为89.66%。

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