KEYWORD RECOMMENDATION METHOD AND SYSTEM BASED ON LATENT DIRICHLET ALLOCATION MODEL

摘要

Keyword recommendation methods and systems based on a latent Dirichlet allocation (LDA) model. The method comprises: calculating a basic Dirichlet allocation model for training texts; obtaining an incremental seed word, and selecting from the training texts a training text matching the incremental seed word to serve as an incremental training text; calculating an incremental Dirichlet allocation model for the incremental training text; obtaining a probability distribution of complete words to topics and a probability distribution of complete texts to topics; calculating a relevance score between the complete word and any other complete word respectively to obtain a relevance score between every two complete words; and determining, according to an obtained query word and the obtained relevance score between every two complete words, a keyword corresponding to the query word. By employing an incremental training model, the present invention greatly improves the precision of topic clustering and topic diversity, and significantly improves the quality of keywords in the topics.