Class-based probability estimation using a semantic hierarchy
Computational Linguistics
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Word clustering and disambiguation based on co-occurrence data
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Random walks on text structures
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A graph-theoretic framework for semantic distance
Computational Linguistics
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Comparing word contexts is a key component of many NLP tasks, but rarely is it used in conjunction with additional ontological knowledge. One problem is that the amount of overhead required can be high. In this paper, we provide a graphical method which easily combines an ontology with contextual information. We take advantage of the intrinsic graphical structure of an ontology for representing a context. In addition, we turn the ontology into a metric space, such that subgraphs within it, which represent contexts, can be compared. We develop two variants of our graphical method for comparing contexts. Our analysis indicates that our method performs the comparison efficiently and offers a competitive alternative to non-graphical methods.