Part-of-speech tagging

被引：16

作者：

Martinez, Angel R. ^{[1
]}

机构：

[1] Strayer Univ, Dept Math, Fredericksburg, VA 22406 USA

来源：

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS | 2012年 / 4卷 / 01期

关键词：

natural language processing; Hidden Markov models; stochastic tagging; rule-based tagging;

D O I：

10.1002/wics.195

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Presented here is a brief state-of-the-art account on part-of-speech (POS) tagging. POS tagging is an essential preprocessing task for many natural language processing goals and applications. Some POS tagging approaches make use of annotated corpora to train computational models to perform the task with minimal human intervention. Rule-based and stochastic methods have been successful, attaining accuracies of 96-97%. Representative approaches of these two methodologies are discussed. (C) 2011 Wiley Periodicals, Inc.

引用

页码：107 / 113

页数：7

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