Paper Info
Title
Key Concept Identification: A Comprehensive Analysis Of Frequency And Topical Graph-Based Approaches
Abstract
Automatic key concept extraction from text is the main challenging task in information extraction, information retrieval and digital libraries, ontology learning, and text analysis. The statistical frequency and topical graph-based ranking are the two kinds of potentially powerful and leading unsupervised approaches in this area, devised to address the problem. To utilize the potential of these approaches and improve key concept identification, a comprehensive performance analysis of these approaches on datasets from different domains is needed. The objective of the study presented in this paper is to perform a comprehensive empirical analysis of selected frequency and topical graph-based algorithms for key concept extraction on three different datasets, to identify the major sources of error in these approaches. For experimental analysis, we have selected TF-IDF, KP-Miner and TopicRank. Three major sources of error, i.e., frequency errors, syntactical errors and semantical errors, and the factors that contribute to these errors are identified. Analysis of the results reveals that performance of the selected approaches is significantly degraded by these errors. These findings can help us develop an intelligent solution for key concept extraction in the future.
Year
DOI
Venue
2018
10.3390/info9050128
INFORMATION
Keywords
Field
DocType
keyphrase extraction, key concept extraction, information retrieval, empirical analysis, text mining
Graph,Text mining,Ranking,Computer science,Frequency,Information extraction,Artificial intelligence,Digital library,Concept extraction,Machine learning,Ontology learning
Journal
Volume
Issue
ISSN
9
5
2078-2489
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Muhammad Aman110.70
Abas Md. Said2848.17
Said Jadid Abdul Kadir310.70
Israr Ullah400.34