A Weighted Topical Document Embedding based Clustering Method for News Text
As an unsupervised machine learning method,clustering can preliminarily group text without artificial labeling,which effectively accelerates the organization,abstraction and navigation on large news set.The length of news is long,and the text contains many homonymy and polysemy,that is one of the reason that traditional text clustering methods perform weaker on grouping news text.This paper presents a novel text representation method based on topical document embedding (TDE) to capture the semantic features of different topics.In TDE representation,document embedding of news texts is obtained by adding up word vector from Skip-Gram model weighted by TFIDF score of all the key words in the text.While the topical document embedding is learned by joining the topic vectors obtained from LDA model and the document vectors in document embedding.By using topical document embedding to perform clustering,we implement a novel text clustering method (TDE-TC).The experimental results show that the effect of news clustering based on TDE representation is better than that of bag of words model and LDA model.
Text Clustering Skip-Gram LDA TF-IDF
Zhu Dechao Song Hui
School of Computer Science Donghua University Shanghai,China
国际会议
重庆
英文
1060-1065
2016-03-20（万方平台首次上网日期，不代表论文的发表时间）