An unsupervised approach to prepositional phrase attachment using contextually similar words

被引：0

作者：

Pantel, P ^{[1
]}

Lin, DK ^{[1
]}

机构：

[1] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2H1, Canada

来源：

38TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Prepositional phrase attachment is a common source of ambiguity in natural language processing. We present an unsupervised corpus-based approach to prepositional phrase attachment that achieves similar performance to supervised methods. Unlike previous unsupervised approaches in which training data is obtained by heuristic extraction of unambiguous examples from a corpus, we use an iterative process to extract training data from an automatically parsed corpus. Attachment decisions are made using a linear combination of features and low frequency events are approximated using contextually similar words.

引用

页码：101 / 108

页数：8

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