All-words word sense disambiguation for Turkish

Onur Açıkgöz, Ali Tunca Gürkan, Burak Ertopçu, Ozan Topsakal, Berke Özenç, Ali Bugra Kanburoglu, Ilker Çam, Begüm Avar, Gökhan Ercan, O. T. Yildiz
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引用次数: 6

Abstract

Identifying the sense of a word within a context is a challenging problem and has many applications in natural language processing. This assignment problem is called word sense disambiguation (WSD). Many papers in the literature focus on English language and data. Our dataset consists of 1400 sentences translated to Turkish from the Penn Treebank Corpus. This paper seeks to address and discuss 6 different feature extraction methods and its classification performances using C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear and multilayer Perceptron. This paper calls into question how the described features perform on a morphologically rich language (Turkish) with several classifiers.
土耳其语全词词义消歧
在上下文中识别单词的意义是一个具有挑战性的问题,并且在自然语言处理中有许多应用。这种分配问题被称为词义消歧(WSD)。文献中的许多论文都集中在英语语言和数据上。我们的数据集由1400个从宾州树库语料库翻译成土耳其语的句子组成。本文试图解决和讨论6种不同的特征提取方法及其分类性能,包括C4.5、随机森林、Rocchio、朴素贝叶斯、KNN、线性和多层感知器。本文提出了一个问题,即描述的特征如何在具有多个分类器的形态学丰富的语言(土耳其语)上执行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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