NDLI: Unsupervised key-phrases extraction from scientific papers using domain and linguistic knowledge

Content Provider	IEEE Xplore Digital Library
Author	Krapivin, M. Marchese, M. Yadrantsau, A. Yanchun Liang
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento (Krapivin, M.; Marchese, M.) \|\| Key Lab. of Symbol Comput. & Knowledge Eng. of Minist. of Educ., Jilin Univ., Changchun (Yanchun Liang) \|\| Dept. of Comput. Sci., Belarussian State Univ., Minsk (Yadrantsau, A.)
Abstract	The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of userspsila navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser) to tackle the problem of unsupervised key-phrases extraction from scientific papers. The proposed method strongly depends on the robust structural properties of a scientific paper as well as on the lexical knowledge that we are able to mine from its text. For the experimental assessment we have use a subset of ACM papers in the Computer Science domain containing 400 documents. Preliminary evaluation of the approach shows promising result that improves - on the same data-set - on state-of-the-art Bayesian learning system KEA from a minimum 27% to a maximum 77% depending on KEA parameters tuning and specific evaluation set. Our assessment is performed by comparison with key-phrases assigned by human experts in the specific domain and freely available through ACM portal.
Starting Page	105
Ending Page	112
File Size	182946
Page Count	8
File Format	PDF
ISBN	9781424429165
DOI	10.1109/ICDIM.2008.4746749
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Software libraries Navigation Bayesian methods Support vector machine classification Machine learning Natural language processing Robustness Data mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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