A Comparison of Corpus-Based Techniques for Restoring Accents in Spanish and French Text
A Comparison of Corpus-Based Techniques for Restoring Accents in Spanish and French Text
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基于语料库的西班牙语和法语文本口音恢复技术比较
DOI：
10.1007/978-94-017-2390-9_7
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
David Yarowsky
中科院分区：
文献类型：
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作者：
David Yarowsky
本章将探索和比较三种基于语料库的词汇歧义消解技术，重点是将缺失的重音恢复到西班牙语和法语文本中的问题。由于缺少重音而产生的许多歧义是词性上的差异：因此，考虑的方法之一是使用维特比解码的N元语法标记器，例如在随机词性标记器中发现的方法。第二种技术，贝叶斯分类，已经成功地应用于词义消歧，并很好地适合于一些由于缺少重音而产生的语义歧义。第三种方法基于决策列表，结合了其他两种方法的优点，既包含了本地句法模式，又包含了更远距离的搭配证据，并且比这两种方法都要好。重音恢复问题特别适合于演示和测试给定算法的能力，因为它需要解决语义和句法歧义，并为自动评估提供客观的基础事实。这个问题也是一个具有现实性和应用性的问题。
This chapter will explore and compare three corpus-based techniques for lexical ambiguity resolution, focusing on the problem of restoring missing accents to Spanish and French text. Many of the ambiguities created by missing accents are differences in part of speech: hence one of the methods considered is an N-gram tagger using Viterbi decoding, such as is found in stochastic part-of-speech taggers. A second technique, Bayesian classification, has been successfully applied to word-sense disambiguation and is well suited for some of the semantic ambiguities which arise from missing accents. The third approach, based on decision lists, combines the strengths of the two other methods, incorporating both local syntactic patterns and more distant collocational evidence, and outperforms them both. The problem of accent restoration is particularly well suited for demonstrating and testing the capabilities of the given algorithms because it requires the resolution of both semantic and syntactic ambiguity, and offers an objective ground truth for automatic evaluation. The problem is also a practical one with immediate application.