Improved Automatic Keyphrase Extraction by Using Semantic Information
Keyphrases provide semantic metadata producing an overview of the content of a document, they are used in many text-mining applications. This paper proposes a new method that improves automatic keyphrase extraction by using semantic information of candidate kevphrases. Our method is realized in two stages. In selecting candidates stage, after extraction of all phrases from document, a word sense disambiguation method is used to get senses of phrases, then term conflation is performed by using case folding, stemming, and semantic relatedness between candidates. In filtering stage, four features are used to compute for each candidate: the TF×lDF measure describing the specificity of a phrase, first occurrence of a phrase in the document, length of a phrase, and coherence score which measure the semantic relatedness between the phrase and other candidates. A Naive Baves scheme builds a prediction model training data with known keyphrases, and then uses the model to calculate the overall probability for each candidate. We evaluate semantically improved method against the well known Kea system by using a more effective semantically enhanced evaluation method. The inter domain experiment shows that quality of keyphrases extraction can be improved significantly when semantic information is exploited The intra-domain experiment shows our method is competitive with Kea++ algorithm, and not domain-specific.
keyphrase extraction word sense disambiguafion semantic information
XiaoLing Wang DeJun Mu Jun Fang
Control and Networks Laboratory, School of Automation, Northwestern Polytechnical University, China
国际会议
长沙
英文
1061-1065
2008-10-20（万方平台首次上网日期，不代表论文的发表时间）