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首页> 外文会议>2016 5th International Conference on Informatics, Electronics and Vision >A supervised approach for word sense disambiguation based on Arabic diacritics
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A supervised approach for word sense disambiguation based on Arabic diacritics

机译：基于阿拉伯变音符号的词义歧义监督方法

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Since the last two decades' Arabic natural language processing (ANLP) has become increasingly much more important. One of the key issues related to ANLP is ambiguity. In Arabic language different pronunciation of one word may have a different meaning. Furthermore, ambiguity also has an impact on the effectiveness and efficiency of Machine Translation (MT). The issue of ambiguity has limited the usefulness and accuracy of the translation from Arabic to English. The lack of Arabic resources makes ambiguity problem more complicated. Additionally, the orthographic level of representation cannot specify the exact meaning of the word. This paper looked at the diacritics of Arabic language and used them to disambiguate an ambiguous word. The proposed approach of word sense disambiguation used Diacritizer application to Diacritize Arabic text. Then find the most accurate sense of an ambiguous word using Naïve Bayes Classifier. Our system gets 91% precision, and 12.11% error rate. This experimental study proves that using Arabic Diacritics with Naïve Bayes Classifier enhances the accuracy of choosing the appropriate sense for ambiguous Arabic words.
机译：自过去的二十年以来，阿拉伯自然语言处理（ANLP）变得越来越重要。与ANLP相关的关键问题之一是歧义。在阿拉伯语中，一个单词的不同发音可能具有不同的含义。此外，歧义性还会影响机器翻译（MT）的有效性和效率。含糊不清的问题限制了从阿拉伯语到英语的翻译的有用性和准确性。阿拉伯资源的缺乏使歧义问题更加复杂。此外，表示法的拼写水平不能指定单词的确切含义。本文研究了阿拉伯语变音符号，并用它们来消除歧义词。提出的单词歧义消除方法使用Diacritizer应用程序对阿拉伯文本进行Diacritize。然后使用朴素贝叶斯分类器找到最准确的歧义词含义。我们的系统具有91％的精度和12.11％的错误率。这项实验研究证明，将阿拉伯变音符号与NaïveBayes分类器结合使用，可以提高为歧义的阿拉伯语单词选择适当含义的准确性。

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