Syntactic N-grams as machine learning features for natural language processing

Jun Ping Ng

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Syntactic N-grams as machine learning features for natural language processing

机译：语法N-gram作为自然语言处理的机器学习功能

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Traditionally, n-grams are derived by extracting groups of words as they appear in a text. This paper describes a new way to formulate n-grams, referred to as syntactic n-grams (sn-grams). Sn-grams are made by extracting groups of words not based on how they appear in a text, but rather on how they are presented in grammar parse trees. Derivations from both constituent grammar parses and typed dependency parses are possible. This difference in derivation is interesting and can potentially have an impact on how n-grams are used.

机译：传统上，n-gram是通过提取出现在文本中的单词组来得出的。本文介绍了一种新的表达n-gram的方法，称为语法n-gram（sn-gram）。 Sn-gram的提取不是基于单词组在文本中的显示方式，而是基于它们在语法分析树中的呈现方式来提取。既可以从组成语法分析又可以从类型化依赖关系分析派生。这种派生差异很有趣，并且可能会影响n-gram的使用方式。

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《Computing reviews》 |2014年第3期|187-188|共2页
作者
Jun Ping Ng;
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正文语种 eng
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THEORY;

机译：理论;

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Syntactic N-grams as machine learning features for natural language processing

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