Khmer POS Tagger: A Transformation-based Approach with Hybrid Unknown Word Handling
Khmer POS Tagger: A Transformation-based Approach with Hybrid Unknown Word Handling
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DOI：
10.1109/icsc.2007.104
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Chenda Nou;W. Kameyama
中科院分区：
文献类型：
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作者：
Chenda Nou;W. Kameyama
本文对高棉语词性标注进行了初步研究。我们对基于转换的方法的规则算法的应用提出了一些修改，以适应在形态和语法上与英语不同的高棉语。此外，为了克服基于规则的方法在处理未登录词的覆盖范围有限，我们提出了一种混合的方法，结合联合收割机的规则和三元模型。虽然在一个非常小的语料库上进行训练，但这两种方法都比传统方法获得了更高的准确率。该标注器在训练数据和测试数据上分别达到了95.27%和91.96%，其中包含9%的未登录词。
This paper presents an initiative research on Khmer part-of-speech tagger. We propose some modifications on applying rule algorithm of the transformation-based approach to adapt to Khmer language which is morphologically and syntactically different from the English language. Furthermore, to overcome the limited coverage of the rule-based approach in handling unknown words, we propose a hybrid approach to combine the rule-based and trigram models. Although training on a very small corpus, both proposed approaches achieve higher accuracy than the conventional methods. The tagger achieves 95.27% on training data and 91.96% on test data which includes 9% of unknown words.