Natural Language Parsing as Statistical Pattern Recognition
Natural Language Parsing as Statistical Pattern Recognition
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发表时间：
1994-05
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通讯作者：
David M. Magerman
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作者：
David M. Magerman
传统的自然语言解析器基于语法学家以艰苦、耗时的方式开发的重写规则系统。语法学家的大部分努力都致力于消歧过程，首先假设规定歧义句子中单词的构成类别和关系的规则，然后寻找这些规则的例外和更正。在这项工作中，我提出了一种从一组解析句子中获取统计解析器的自动方法，该方法利用了一些初始语言输入，但避免了迭代和看似无休止的语法开发过程的陷阱。基于语言的分布导出和基于语言的特征，该解析器获取一组统计决策树，这些决策树在给定输入句子的解析树空间上分配概率分布。这些决策树利用大量上下文信息（可能包括句子中的所有词汇信息）来生成高度准确的消歧过程统计模型。通过将消歧标准选择基于熵减少而不是人类直觉，这种解析器开发方法在制定单独的消歧规则时能够比人类语法学家考虑更多的句子。在使用此统计框架获得的解析器和语法学家基于规则的解析器（历时十年开发）之间的实验中，都使用相同的训练材料和测试句子，决策树解析器在语法学家试图最大化的准确度测量上显着优于基于语法的解析器，达到了 78% 的准确度，而基于语法的解析器的准确度为 69%。
Traditional natural language parsers are based on rewrite rule systems developed in an arduous, time-consuming manner by grammarians. A majority of the grammarian's efforts are devoted to the disambiguation process, first hypothesizing rules which dictate constituent categories and relationships among words in ambiguous sentences, and then seeking exceptions and corrections to these rules. In this work, I propose an automatic method for acquiring a statistical parser from a set of parsed sentences which takes advantage of some initial linguistic input, but avoids the pitfalls of the iterative and seemingly endless grammar development process. Based on distributionally-derived and linguistically-based features of language, this parser acquires a set of statistical decision trees which assign a probability distribution on the space of parse trees given the input sentence. These decision trees take advantage of significant amount of contextual information, potentially including all of the lexical information in the sentence, to produce highly accurate statistical models of the disambiguation process. By basing the disambiguation criteria selection on entropy reduction rather than human intuition, this parser development method is able to consider more sentences than a human grammarian can when making individual disambiguation rules. In experiments between a parser, acquired using this statistical framework, and a grammarian's rule-based parser, developed over a ten-year period, both using the same training material and test sentences, the decision tree parser significantly outperformed the grammar-based parser on the accuracy measure which the grammarian was trying to maximize, achieving an accuracy of 78% compared to the grammar-based parser's 69%.