关键词: 关键词提取, 词向量, 位置信息, PageRank算法

Abstract:

Focused on the issue that the existing graph-based keyphrase extraction methods fail to integrate the potential semantic relationship among words in text sequences, a graph-based keyphrase extraction algorithm EPRank that integrates word embeddings and position information is proposed. First, the word embedding of each word in the target document is learned by the word embedding representation model. Secondly, the word embeddings which reflect the potential semantic relationship among words and position information are combined into the PageRank scoring model. Finally, it selects a few top-ranked words or phrases as keyphrases for the target document. The experimental results show that the proposed algorithm EPRank can achieve higher values in terms of every evaluation metric on KDD and SIGIR datasets than the five existing keyphrase extraction methods.

Key words: keyphrase extraction, word embedding, position information, PageRank algorithm