基于千亿词语料库的语义倾向性无监督学习
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
摘要：
一个词的评价性特征被称为它的语义指向。一个积极的语义取向意味着可取性（例如，“honest”，“intrepid”），而否定的语义取向意味着不受欢迎（例如，“令人不安的”，“多余的”）。本文介绍了一个简单的算法，无监督学习的语义倾向，从极大的语料库。该方法包括向Web搜索引擎发出查询，并使用逐点互信息来分析结果。该算法是使用大约1000亿个单词的训练语料库进行经验评估的--该语料库是由所选搜索引擎索引的Web子集。测试了3，596个单词（1，614个阳性和1，982个阴性），该算法达到了80%的准确率。3,596个测试单词包括形容词、副词、名词和动词。准确性与Hatzivassiloglou和McKeown（1997）的结果相当，使用复杂的四阶段监督学习算法，仅限于确定形容词的语义方向。
英文摘要：
The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words -- the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives.
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。
