基于短语的柬汉双语LDA主题模型

为了有效地获取双语文档的主题分布，提出了一种基于短语的柬汉双语LDA主题模型。修改了传统LDA主题模型中的词袋模型，融入短语（N-gram）的概念，能够在主题预测过程中考虑文章的词序以及上下文，并将之应用于可比语料的双语环境中。本模型基于一个3层贝叶斯网络模型，在此框架下，首先搜集中文和柬埔寨语的可比语料，每一对双语可比语料文档共享一个相同的主题分布，之后引入发现主题以及主题短语的主题模型：对每个单词，首先进行主题抽样，然后将其状态作为短语进行采样，最后对来自特定主题短语分布的单词进行采样。通过实验结果可知，基于短语的双语LDA主题模型比一般的双语LDA模型更能抓住文章的主题，且有更好的主题预测能力。

A phrase-based Khmer-Chinese bilingual LDA topic model construction method

In order to obtain the topic distribution of bilingual documents effectively, we propose a phrase-based Khmer-Chinese bilingual LDA topic model. We modify the bag-of-word model in the traditional LDA topic model and incorporate the concept of phrase (N-gram). The method considers the word order and context of the article in the topic prediction process and applies it to the bilingual environment of comparable corpus. It is based on a three-layer Bayesian network model. Under this framework, we firstly collect comparable Chinese and Khmer corpus, and each pair of bilingual comparable corpus shares a common topic distribution. And then we introduce the topic model of discovery topic and topic phrase: the topic of each word is firstly sampled; then its status is sampled as a phrase; and finally words from a particular topic phrase distribution are sampled. Experimental results show that the phrase-based bilingual LDA topic model is more capable of grasping the topic of the article than general bilingual LDA models and has better topic prediction ability.