Paper Info
Title
Automatically Generating a Concept Hierarchy with Graphs
Abstract
We propose a novel graph-based approach for constructing concept hierarchy from a large text corpus. Our algorithm incorporates both statistical co-occurrences and lexical similarity in optimizing the structure of the taxonomy. To automatically generate topic-dependent taxonomies from a large text corpus, we first extracts topical terms and their relationships from the corpus. The algorithm then constructs a weighted graph representing topics and their associations. A graph partitioning algorithm is then used to recursively partition the topic graph into a taxonomy. For evaluation, we apply our approach to articles, primarily computer science, in the CiteSeerX digital library and search engine.
Year
DOI
Venue
2015
10.1145/2756406.2756967
ACM/IEEE Joint Conference on Digital Libraries
Field
DocType
Citations 
Lexical similarity,Search engine,Computer science,Text corpus,Theoretical computer science,Artificial intelligence,Natural language processing,Digital library,Partition (number theory),Graph partition,Recursion,Graph (abstract data type)
Conference
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Pucktada Treeratpituk117711.12
Madian Khabsa223718.81
C. Lee Giles3111541549.48