W-POS语言模型及其选择与匹配算法

n-grams语言模型旨在利用多个词的组合形式生成文本特征,以此训练分类器对文本进行分类。然而n-grams自身存在冗余词,并且在与训练集匹配量化的过程中会产生大量稀疏数据,严重影响分类准确率,限制了其使用范围。对此,基于n-grams语言模型,提出一种改进的n-grams语言模型--W-POS。将分词后文本中出现概率较小的词和冗余词用词性代替,得到由词和词性的不规则排列组成的W-POS语言模型,并提出该语言模型的选择规则、选择算法以及与测试集的匹配算法。在复旦大学中文语料库和英文语料库20Newsgroups中的实验结果表明,W-POS语言模型既继承了n-grams语言模型减少特征数量、携带部分语义和提高精度的优点,又克服了n-grams语言模型产生大量稀疏数据、含有冗余词的缺陷,并验证了选择和匹配算法的有效性。

W-POS language model and its selecting and matching algorithms

n-grams language model aims to use text feature combined of some words to train classifier. But it contains many redundancy words, and a lot of sparse data will be generated when n-grams matches or quantifies the test data, which badly influences the classification precision and limites its application. Therefore, an improved language model named W-POS (Word-Parts of Speech) was proposed based on n-grams language model. After words segmentation, parts of speeches were used to replace the words that rarely appeared and were redundant, then the W-POS language model was composed of words and parts of speeches. The selection rules, selecting algorithm and matching algorithm of W-POS language model were also put forward. The experimental results in Fudan University Chinese Corpus and 20Newsgroups show that the W-POS language model can not only inherit the advantages of n-grams including reducing amount of features and carrying parts of semantics, but also overcome the shortages of producing large sparse data and containing redundancy words. The experiments also verify the effectiveness and feasibility of the selecting and matching algorithms.