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(ICWS 2020) Keyphrase Extraction in Scholarly Digital Library Search Engines

About This Webinar

Abstract: Scholarly digital libraries provide access to scientific publications and comprise useful resources for researchers who search for literature on specific subject areas. CiteSeerX is an example of such a digital library search engine that provides access to more than 10 million academic documents and has nearly one million users and three million hits per day. Artificial Intelligence (AI) technologies are used in many components of CiteSeerX including Web crawling, document ingestion, and metadata extraction. CiteSeerX also uses an unsupervised algorithm called noun phrase chunking (NP-Chunking) to extract keyphrases out of documents. However, often NP-Chunking extracts many unimportant noun phrases. In this paper, we investigate and contrast three supervised keyphrase extraction models to explore their deployment in CiteSeerX for extracting high quality keyphrases. To perform user evaluations on the keyphrases predicted by different models, we integrate a voting interface into CiteSeerX. We show the development and deployment of the keyphrase extraction models and the maintenance requirements.

Authors: Krutarth I Patel (Kansas State University, USA); Cornelia Caragea (University of Illinois at Chicago, USA); Jian Wu (Old Dominion University, USA); C. Lee Giles (Pennsylvania State University, USA)

Email: kipatel@ksu.edu, cornelia@uic.edu, jwu@cs.odu.edu, giles@ist.psu.edu

Who can view: Everyone
Webinar Price: Free
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Krutarth Patel is currently a Ph.D. candidate in Computer Science at Kansas State University. His doctoral research includes different information retrieval and machine learning or deep learning-based problems such as keyphrase extraction, document classification, researcher homepage classification, researcher homepage finding, etc. For his Ph.D. thesis, he is working under the supervision of Dr. Cornelia Caragea. During his free time, he enjoys listening to instrumental music and cooking.
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