Learning by Examples: Identifying Key Concepts from Text Using Pre-defined Inputs

摘要

This article describes a keyphrase identification program (KIP) which extracts document key concepts by using sample human keyphrases. KIP considers the composition of a keyphrase. The more keywords a phrase contains and the more significant these keywords are, the more likely this phrase is a keyphrase. KIP first populates its database using manually identified keyphrases and keywords; it then checks the composition of all identified noun phrases, looks up the database and calculates scores for all these noun phrases; the ones having higher scores will be extracted as keyphrases. KIP's learning function can enrich the database by automatically adding new keyphrases to the database. Consequently, the database will grow gradually and the system performance will be improved. The results from our small-scale preliminary experiments show that KIP is effective in extracting document keyphrases and its learning function is useful.