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Noun Sense Identification of Korean Nominal Compounds Based on Sentential Form Recovery

机译:基于句子形式恢复的朝鲜族名词性名词名词意义识别

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摘要

In a machine translation system, word sense disambiguation has an essential role in the proper translation of words when the target word can be translated differently depending on the context. Previous research on sense identification has mostly focused on adjacent words as context information. Therefore, hi the case of nominal compounds, sense tagging of unit nouns mainly depended on other nouns surrounding the target word. In this paper, we present a practical method for the sense tagging of Korean unit nouns in a nominal compound. To overcome the weakness of traditional methods regarding the data sparseness problem, the proposed method adopts complement-predicate relation knowledge that was constructed for machine translation systems. Our method is based on a sentential form recovery technique, which recognizes grammatical relationships between unit nouns. This technique makes use of the characteristics of Korean predicative nouns. To show that our method is effective on text in general domains, the experiments were performed on a test set randomly extracted from article titles hi various newspaper sections.
机译:在机器翻译系统中,当目标词可以根据上下文进行不同的翻译时,词义歧义消除对词的正确翻译至关重要。先前关于感觉识别的研究主要集中在相邻词作为上下文信息。因此,在名词性化合物的情况下,单位名词的意义标记主要取决于目标词周围的其他名词。在本文中,我们提出了一种实用的方法,用于在名词性复合词中对韩国单位名词进行意义标记。为了克服传统方法在数据稀疏性问题上的缺点,该方法采用了为机器翻译系统构建的补语谓语关系知识。我们的方法基于句子形式恢复技术,该技术可识别单位名词之间的语法关系。该技术利用了朝鲜语谓语名词的特征。为了证明我们的方法对一般领域的文本有效,我们对从报纸各版面的文章标题中随机抽取的测试集进行了实验。

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