基于句法的词性分析器
A syntax-based part-of-speech analyser
摘要：
构建自然语言分析器的知识库主要有两种方法：语言学方法和数据驱动方法。最近的国家的最先进的词性标注是基于数据驱动的方法。由于已知的语言规则为基础的方法在相关的描述水平的可行性，数据驱动的方法在词性分析的成功可能会出现令人惊讶的。在本文中，一个情况下作出的句法性质的词性标注。一个新的tagger的英语，只使用语言分布规则的概述和经验评估。在一个包含38，000个单词的基准语料库中进行测试，这个基于语法的系统达到了99%以上的准确率。与其最佳竞争对手95-97%的准确率相比，这一结果表明语言学方法在词性分析中也是可行的。
英文摘要：
There are two main methodologies for constructing the knowledge base of a natural language analyser: the linguistic and the data-driven. Recent state-of-the-art part-of-speech taggers are based on the data-driven approach. Because of the known feasibility of the linguistic rule-based approach at related levels of description, the success of the data-driven approach in part-of-speech analysis may appear surprising. In this paper, a case is made for the syntactic nature of part-of-speech tagging. A new tagger of English that uses only linguistic distributional rules is outlined and empirically evaluated. Tested against a benchmark corpus of 38,000 words of previously unseen text, this syntax-based system reaches an accuracy of above 99%. Compared to the 95-97% accuracy of its best competitors, this result suggests the feasibility of the linguistic approach also in part-of-speech analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
