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WEKE: Learning Word Embeddings for Keyphrase Extraction
- Source :
- Web and Big Data ISBN: 9783030602895, APWeb/WAIM (2)
- Publication Year :
- 2020
- Publisher :
- Springer International Publishing, 2020.
-
Abstract
- Traditional supervised keyphrase extraction models depend on the features of labeled keyphrases while prevailing unsupervised models mainly rely on global structure of the word graph, with nodes representing candidate words and edges/links capturing the co-occurrence between words. However, the local context information of the word graph can not be exploited in existing unsupervised graph-based keyphrase extraction methods and integrating different types of information into a unified model is relatively unexplored. In this paper, we propose a new word embedding model specially for keyphrase extraction task, which can capture local context information and incorporate them with other types of crucial information into the low-dimensional word vector to help better extract keyphrases. Experimental results show that our method consistently outperforms 7 state-of-the-art unsupervised methods on three real datasets in Computer Science area for keyphrase extraction.
- Subjects :
- 050101 languages & linguistics
Word embedding
Computer science
business.industry
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
05 social sciences
02 engineering and technology
computer.software_genre
Word graph
0202 electrical engineering, electronic engineering, information engineering
Graph (abstract data type)
020201 artificial intelligence & image processing
0501 psychology and cognitive sciences
Extraction methods
Artificial intelligence
Global structure
business
computer
Natural language processing
Subjects
Details
- ISBN :
- 978-3-030-60289-5
- ISBNs :
- 9783030602895
- Database :
- OpenAIRE
- Journal :
- Web and Big Data ISBN: 9783030602895, APWeb/WAIM (2)
- Accession number :
- edsair.doi...........32d867f5d1cfa602e9cb7cbd62ba4133