Paper Info
Title
Domain-specific semantic relatedness from Wikipedia: can a course be transferred?
Abstract
Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or CYC that requires intensive manual efforts to build and maintain. Other work is based on the Brown corpus, or more recently, Wikipedia. Wikipedia-based measures, however, typically do not take into account the rapid growth of that resource, which exponentially increases the time to prepare and query the knowledge base. Furthermore, the generalized knowledge domain may be difficult to adapt to a specific domain. To address these problems, this paper proposes a domain-specific semantic relatedness measure based on part of Wikipedia that analyzes course descriptions to suggest whether a course can be transferred from one institution to another. We show that our results perform well when compared to previous work.
Year
Venue
Keywords
2012
HLT-NAACL
sparse knowledge base,analyzes course description,generalized knowledge domain,specific domain,knowledge base,domain-specific semantic relatedness measure,related work,semantic relatedness,previous work,domain-specific semantic relatedness,semantic distance
Field
DocType
Citations 
Semantic similarity,Computer science,Closeness,Explicit semantic analysis,Natural language processing,Artificial intelligence,Knowledge base,WordNet,Machine learning,Semantics,Brown Corpus
Conference
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Beibei Yang111.37
Jesse M. Heines24114.06