Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Sentence Similarity Based on Semantic Nets and Corpus Statistics
IEEE Transactions on Knowledge and Data Engineering
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Ontology evaluation using wikipedia categories for browsing
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Introduction to Information Retrieval
Introduction to Information Retrieval
Knowledge derived from wikipedia for computing semantic relatedness
Journal of Artificial Intelligence Research
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Measuring semantic distance using distributional profiles of concepts
Measuring semantic distance using distributional profiles of concepts
Enhancing the open-domain classification of named entity using linked open data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Machine reading: from wikipedia to the web
Machine reading: from wikipedia to the web
Using semantic distance to automatically suggest transfer course equivalencies
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
| Hi-index | 0.00 |
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.