NDLI: A novel approach to keyphrase extraction using augmented transition networks and statistical tools

Content Provider	ACM Digital Library
Author	Bahl, Akshay Mishra, Anirvana Singh, Gaurav
Abstract	We present a novel approach to extract keyphrases based on Augmented Transition Networks (abbreviated as ATNs) followed by statistical methods from any given article, notes on a particular subject, or any other document source. The use of ATNs has completely ruled out the need of background corpora in identifying the potential keywords and keyphrases. Moreover, the use of ATNs has greatly reduced the search space for the statistical methods. We have devised two new methods namely, relaxed statistical analysis and stringent statistical analysis to identify the separability of phrases into sub phrases. In this paper, the two tier process is discussed in detail and illustrated with examples. We have also discussed the applications of this process briefly.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450307505
DOI	10.1145/1980422.1980455
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text mining Augmented transition networks Keyphrase extraction Statistical analysis Keyphrase mining Word collocation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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A novel approach to keyphrase extraction using augmented transition networks and statistical tools