Foundations of statistical natural language processing
Foundations of statistical natural language processing
Modern Information Retrieval
Toward knowledge-free induction of machine-readable dictionaries
Toward knowledge-free induction of machine-readable dictionaries
Co-occurrences of antonymous adjectives and their contexts
Computational Linguistics
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Termight: identifying and translating technical terminology
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Knowledge-free discovery of domain-specific multiword units
Proceedings of the 2008 ACM symposium on Applied computing
BorderFlow: A Local Graph Clustering Algorithm for Natural Language Processing
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Clique-based clustering for improving named entity recognition systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
GRAONTO: A graph-based approach for automatic construction of domain ontology
Expert Systems with Applications: An International Journal
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Terminology extraction is an essential step in several fields of natural language processing such as dictionary and ontology extraction. In this paper, we present a novel graph-based approach to terminology extraction. We use SIGNUM, a general purpose graph-based algorithm for binary clustering on directed weighted graphs generated using a metric for multi-word extraction. Our approach is totally knowledge-free and can thus be used on corpora written in any language. Furthermore it is unsupervised, making it suitable for use by non-experts. Our approach is evaluated on the TREC-9 corpus for filtering against the MESH and the UMLS vocabularies.