Keyword extraction using multiple novel features
|Keyword extraction using multiple novel features|
|Author(s)||Yang S., Zhang B., Li S., Yu C., Hao Q.|
|Published in||Journal of Computational Information Systems|
|Keyword(s)||Keyword extraction, Natural language processing (Extra: Natural language processing systems, Classification models, Keyword extraction, NAtural language processing, Wikipedia knowledge, Extraction)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of journal articles|
In this paper, we propose a novel approach for keyword extraction. Different from previous keyword extraction methods, which identify keywords based on the document alone, this approach introduces Wikipedia knowledge and document genre to extract keywords from the document. Keyword extraction is accomplished by a classification model utilizing not only traditional word based features but also features based on Wikipedia knowledge and document genre. In our experiment, this novel keyword extraction approach outperforms previous models for keyword extraction in terms of precision-recall metric and breaks through the plateau previously reached in the field. © 2014 Binary Information Press.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.