对称和非对称词语聚类模型的比较研究

词语聚类是语音识别、智能信息检索等领域的一个重要的自然语言处理问题。实现基于互信息的对称聚类模型，并针对该模型未考虑词语顺序的缺陷，提出一种新的非对称聚类模型。按照聚类词相对其他词语的位置关系，该模型分为2个子模型，即条件聚类模型和预测聚类模型。在大规模数据集上的实验表明，相对于对称聚类模型，非对称聚类模型是一种更为有效的词语聚类模型。

Comparative Research on Symmetric and Asymmetric Word Clustering Models

Word clustering is one of important natural language processing issues in speech recognition and intelligent information retrieval,etc.This paper presents a symmetric clustering model based on mutual information.For the model not taking the order of words into account,it proposes a new asymmetric clustering model including two sub models,conditional clustering model and predictive clustering model.Experimental results on large scale data set show that compared with the symmetric clustering model,the asymmet...