Abstract
Keyphrase extraction plays an important role in many applications of Natural Language Processing. There are many effective proposals for English, but those approaches are not completely applicable for low resources languages such as Vietnamese. In this paper, we propose a Semantic-based Approach for Keyphrase Extraction (SAKE), which improved the TextRank algorithm [1]. In SAKE, we apply semantic to the phrases and incorporates the semantic to the ranking process. Technically, a document is represented as a graph, in which vertices are words and edges are relations among words. In each document, we get a representative thematic vector by computing the average of word embedding vectors. Each vertex has a similarity score to the thematic vector and this score will be involved to the scoring in the ranking process. The important vertices are highly weighted not only by their relationships to other vertices but also by the similarity to the document theme. We experimented our proposed method on Vietnamese news articles. The result shows that our SAKE improved TextRank for Vietnamese text by achieving 1.8% higher of F1-score.
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Le, L.V., Le, T.T.N. (2022). A Semantic-Based Approach for Keyphrase Extraction from Vietnamese Documents Using Thematic Vector. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_33
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