基于SVM的越南语分词：歧义消解和后缀捕捉
Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix Capture
摘要：
在本文中，我们将越南语分词作为一个二元分类，使用支持向量机分类器。我们继承了以前的作品，如音节的n-gram，音节类型的n-gram，并检查字典中相邻音节的连接。我们提出了两种新的特征提取方法，一种是减少重叠歧义，另一种是增加对含有后缀的未登录词的预测能力。与UETsegmenter和RDRsegmenter这两种最先进的越南语分词方法不同，我们不使用最长匹配算法作为初始处理步骤或任何后处理技术。根据越南基准数据集上的实验结果，我们提出的方法比现有的最先进的方法UETsegmenter和RDRsegmenter获得了更好的F1分数。
英文摘要：
In this paper, we approach Vietnamese word segmentation as a binary classification by using the Support Vector Machine classifier. We inherit features from prior works such as n-gram of syllables, n-gram of syllable types, and checking conjunction of adjacent syllables in the dictionary. We propose two novel ways to feature extraction, one to reduce the overlap ambiguity and the other to increase the ability to predict unknown words containing suffixes. Different from UETsegmenter and RDRsegmenter, two state-of-the-art Vietnamese word segmentation methods, we do not employ the longest matching algorithm as an initial processing step or any post-processing technique. According to experimental results on benchmark Vietnamese datasets, our proposed method obtained a better F1-score than the prior state-of-the-art methods UETsegmenter, and RDRsegmenter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
