类比、同义词、反义词和联想的统一方法
A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations
摘要：
识别类比、同义词、反义词和关联似乎是四个不同的任务，需要不同的NLP算法。在过去，这四个任务一直被独立处理，使用各种各样的算法。然而，这四个语义类只是语义现象的一个小样本，我们不能为每个语义现象创建专门的算法;我们需要寻求一种统一的方法。我们建议在类比下对广泛的现象进行归纳。为了限制本文的范围，我们将注意力限制在同义词、反义词和联想的包含上。我们介绍了一种基于监督语料库的机器学习算法，用于分类相似的单词对，我们表明，它可以解决多项选择SAT类比问题，托福同义词问题，ESL同义词-反义词问题，和相似关联-都来自认知心理学的问题。
英文摘要：
Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.
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。
