TY - CHAP
T1 - BioKeySpotter
T2 - An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection
AU - Song, Min
AU - Tanapaisankit, Prat
PY - 2012
Y1 - 2012
N2 - Extracting keyphrases from full-text is a daunting task in that many different concepts and themes are intertwined and extensive term variations exist in full-text. In this chapter, we proposes a novel unsupervised keyphrase extraction system, BioKeySpotter, which incorporates lexical syntactic features to weigh candidate keyphrases. The main contribution of our study is that BioKeySpotter is an innovative approach for combining Natural Language Processing (NLP), information extraction, and integration techniques into extracting keyphrases from full-text. The results of the experiment demonstrate that BioKeySpotter generates a higher performance, in terms of accuracy, compared to other supervised learning algorithms.
AB - Extracting keyphrases from full-text is a daunting task in that many different concepts and themes are intertwined and extensive term variations exist in full-text. In this chapter, we proposes a novel unsupervised keyphrase extraction system, BioKeySpotter, which incorporates lexical syntactic features to weigh candidate keyphrases. The main contribution of our study is that BioKeySpotter is an innovative approach for combining Natural Language Processing (NLP), information extraction, and integration techniques into extracting keyphrases from full-text. The results of the experiment demonstrate that BioKeySpotter generates a higher performance, in terms of accuracy, compared to other supervised learning algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84885630694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885630694&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23151-3_3
DO - 10.1007/978-3-642-23151-3_3
M3 - Chapter
AN - SCOPUS:84885630694
SN - 9783642231506
T3 - Intelligent Systems Reference Library
SP - 19
EP - 27
BT - Data Mining
A2 - Holmes, Dawn
A2 - Jain, Lakhmi
ER -