Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Identifying word correspondence in parallel texts
HLT '91 Proceedings of the workshop on Speech and Natural Language
Pathfinder associative networks: studies in knowledge organization
Pathfinder associative networks: studies in knowledge organization
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
Distribution of content words and phrases in text and language modelling
Natural Language Engineering
Empirical estimates of adaptation: the chance of two noriegas is closer to p/2 than p2
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Parsing, word associations and typical predicate-argument relations
HLT '89 Proceedings of the workshop on Speech and Natural Language
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Computational Linguistics
Determining termhood for learning domain ontologies using domain prevalence and tendency
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Determining termhood for learning domain ontologies in a probabilistic framework
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Design and realization of advertisement promotion based on the content of webpage
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
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We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.