Experts' retrieval with multiword-enhanced author topic model
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Best topic word selection for topic labelling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatic labelling of topic models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic labeling hierarchical topics
Proceedings of the 21st ACM international conference on Information and knowledge management
Unsupervised graph-based topic labelling using dbpedia
Proceedings of the sixth ACM international conference on Web search and data mining
Beyond term clusters: assigning Wikipedia concepts to scientific documents
Proceedings of the 2013 ACM symposium on Document engineering
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An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging, while particularly interesting and significant labeling cases emerged