Modeling ESL Word Choice Similarities By Representing Word Intensions and Extensions

Automatic error correction systems for English as a Second Language(ESL) speakers often rely on the use of a confusion set to limit the choices of possible correction candidates. Typically, the confusion sets are either manually constructed or extracted from a corpus of manually corrected ESL writings. Both options require the involvement of English teachers. This paper proposes a method to automatically construct confusion sets for commonly used prepositions from non-ESL corpus without manual intervention. The proposed method simulates how ESL learners learn both the intensions and extensions of English words from standard English text. Our experimental results suggest that the automatically constructed confusion sets based on the similarities between the learned words’ intensions is competitive with those directly learned from an ESL corpus containing about 150K preposition usages. TITLE AND ABSTRACT IN ANOTHER LANGUAGE, L2 (OPTIONAL, AND ON SAME PAGE) 通过分析单词的内涵和外延来对用词混淆建模 针对把英语作为第二语言的人群的自动语法纠错系统,通常会需要使用“混淆集” 来限制系统纠错的种类。一般来说,这些混淆集或者是由专家总结经验得出,或者是从被 专家纠错过的文字当中提取的。这两种方法都需要英语专家的介入。在这篇论文当中,我 们提出了一种无需专家介入,自动建立常用介词混淆集的方法。在此方法中,我们对英语 单词的内涵和外延建模,并且模拟了英语学习者们学习单词内涵和外延的过程。实验表 明,使用单词内涵之间的相似度来创建的混淆集,与从含15万介词的标注语料当中提取的 混淆集质量是相当的。