A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Building a web thesaurus from web link structure
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
A Thesaurus Construction Method from Large ScaleWeb Dictionaries
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
Web-Based Measure of Semantic Relatedness
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Language-Independent Set Expansion of Named Entities Using the Web
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Graph-based analysis of semantic drift in Espresso-like bootstrapping algorithms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Wikipedia mining for an association web thesaurus construction
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
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In this paper, we propose a method which acquires related words (entities) from multiple words by naturally disambiguating their meaning and considering their contexts. In addition, we introduce a bootstrapping method for improving the coverage of association relations. Experimental result shows that our method can acquire related words depending on the contexts of multiple words compared to the ESA-based method.