Yining Zhao
ABSTRACT
Keyphrase extraction technology can obtain the main content and semantic expression of academic literature, which plays an essential role in text retrieval, classification and clustering. We propose a new method, FeturesRank, to automatically identify meaningful and authoritative keyphrases from Chinese academic texts. FeturesRank integrates three features of keyphrase: frequency, contextual relevance and grammatical relation to measure the likelihood of sequence of words to be a meaningful phrase and introduces a scoring mechanism that combines the influence of words in the network graph with a new “phraseness” feature to calculate a normalized score for every candidate. The experimental results show that the evaluation indexes of the proposed method on Chinese academic datasets are significantly improved compared with the four popular keyphrase extraction methods, which verifies the effectiveness of the method.
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Index Terms
- FeaturesRank: An unsupervised keyphrase extraction approach based on features representation for Chinese documents
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