KeyVector: Unsupervised Keyphrase Extraction Using Weighted Topic via Semantic Relatedness



Document title: KeyVector: Unsupervised Keyphrase Extraction Using Weighted Topic via Semantic Relatedness
Journal: Computación y sistemas
Database:
System number: 000560463
ISSN: 1405-5546
Authors: 1
1
1
Institutions: 1Institute of Information and Computational Technologies, Almaty. Kazajstán
Year:
Season: Jul-Sep
Volumen: 23
Number: 3
Pages: 861-869
Country: México
Language: Inglés
Document type: Artículo
English abstract Keyphrase extraction is a task of automatically selecting topical phrases from a document. We present KeyVector, an unsupervised approach with weighted topics via semantic relatedness for keyphrase extraction. Our method relies on various measures of semantic relatedness of documents, topics and keyphrases in the same vector space, which allow us to compute three keyphrase ranking scores: global semantic score, find more important keyphrases for a given document by measuring the semantic relation between documents and keyphrase embeddings; topic weight, pruning/selecting the candidate keyphrases on the topic level; topic inner score, ranking the keyphrases inside each topic. Keyphrases are then generated by ranking the values of combined three scores for each candidate. We conducted experiments on three evaluation data sets of different length documents and domains. Results show that KeyVector outperforms state of the art methods on short, medium and long documents.
Disciplines: Ciencias de la computación
Keyword: Inteligencia artificial
Keyword: Keyphrase extraction,
Clustering,
Topic modeling,
Semantic relatedness,
Text mining,
Artificial intelligence
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