A vector space model for automatic indexing
Communications of the ACM
Evaluating the novelty of text-mined rules using lexical knowledge
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Building Hypertext Links By Computing Semantic Similarity
IEEE Transactions on Knowledge and Data Engineering
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic Discovery of Part-Whole Relations
Computational Linguistics
Neural Network Based Document Clustering Using WordNet Ontologies
International Journal of Hybrid Intelligent Systems
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Concept Forest: A New Ontology-assisted Text Document Similarity Measurement Method
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Return to Babel: Emergent Diversity, Digital Resources, and Local Knowledge
The Information Society
Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
A metric-based framework for automatic taxonomy induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
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For most, the web is the first source to answer a question formulated by curiosity, need, or research reasons. This phenomenon is due to the internet's ubiquitous access, ease of use, and the extensive and ever expanding content. The problem is no longer the need to acquire content to encourage use, but to provide organizational tools to support content categorization that will facilitate improved access methods. This paper presents the results of a new text characterization algorithm that combines semantic and linguistic techniques utilizing domain-based ontology background knowledge. It explores the combination of meronym, synonym, and hypernym linguistic relationships to create a set of concept chains used to represent concepts found in a document. The experiments show improved accuracy over bag-of-words based term weighting methods and reveal characteristics of the meronym contribution to document representation.