利用二元图的特征向量推断词素同一性
Using eigenvectors of the bigram graph to infer morpheme identity
摘要：
本文描述了一些实验的结果，探索统计方法来推断句法行为的词和词素从一个原始语料库在一个无监督的方式。它与Brown et al（1992）以及从其发展而来的工作有某些共同点：它采用统计技术来分析句法行为，基于与给定单词相邻的单词。然而，我们使用的特征向量分解的最近邻图产生一个二维渲染的单词的语料库中，相同的句法类别的话往往形成邻里。我们利用这种技术扩展形态学自动学习的价值。特别地，我们通过形态学的无监督学习来查看从语料库中导出的后缀，并且我们询问这些后缀中的哪些具有一致的句法功能（例如，在英语中，-tion主要是名词标记，但-s既标记名词复数又标记动词上的第三人称），我们确定此方法适用于此任务。
英文摘要：
This paper describes the results of some experiments exploring statistical methods to infer syntactic behavior of words and morphemes from a raw corpus in an unsupervised fashion. It shares certain points in common with Brown et al (1992) and work that has grown out of that: it employs statistical techniques to analyze syntactic behavior based on what words occur adjacent to a given word. However, we use an eigenvector decomposition of a nearest-neighbor graph to produce a two-dimensional rendering of the words of a corpus in which words of the same syntactic category tend to form neighborhoods. We exploit this technique for extending the value of automatic learning of morphology. In particular, we look at the suffixes derived from a corpus by unsupervised learning of morphology, and we ask which of these suffixes have a consistent syntactic function (e.g., in English, -tion is primarily a mark of nouns, but -s marks both noun plurals and 3rd person present on verbs), and we determine that this method works well for this task.
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。
