Text Topic Number Evaluation Based on Bayes Information Criteria
Shi Zhao-xiang · Jisuanji gongcheng · 2009
There are many applications that can benefit from topic identification and keyword extraction.The traditional way of choosing the topic number depends on human labeling or automatic clustering which is immeasurable.This paper utilizes the Bayes Information Criteria(BIC) based model selection theory to evaluate the probability of each topic numbers taking.After the topic number is acquired, the paper implements the Independent Component Analysis(ICA) decomposition of term-document, then calculates the weight and extracts the keyword according to the ICA separating matrix.Experiments show this method extracts the keyword in a meaningful way.