Statutes Recommendation Based on Text Similarity

Abstract

The traditional approach to measure text similarity is based on the TF-IDF algorithm to get the document vector, and then use the cosine similarity algorithm to calculate the text similarity. However, this method of statistical way ignores the potential semantics of the articles or words. By some means, this method only aims at the word itself. But with the Latent Semantic Analysis, the semantic space is added on the basis of calculate TF-IDF. Each word and document can have a position in semantic space by Singular Value Decomposition. That allows the semantic analysis, document clustering, and the relationship between semantic class and document class can be finished at the same time. Here, we summarize the text similarity measures, and gradually extend to the Latent Semantic Analysis. The experiment shows that the statutes predicted by LSA are more accurate than that only by TF-IDF.