KIP: a keyphrase identification program with learning functions

We report a keyphrase identification program (KIP), which uses sample human keyphrases and then learns to identify additional new keyphrases. KIP first populates its database using manually identified keyphrases; each keyphrase is preprocessed and assigned an initial weight. It then extracts noun phrases from documents. All noun phrases will be assigned a score, depending on the weights for words it contains; the ones that have a score higher than the threshold will be selected as keyphrases. Learned new keyphrases will be inserted to the database and weights will be updated. As a result, new keyphrase identification iteration will be triggered. The process stops when no new keyphrases are identified during previous iteration. According to the results of evaluation, the base KIP system´s average recall was 0.7 and precision was 0.44. The augmented KIP with learning functions did produce new keyphrases which were not identified by the base system.