A practical web-based approach to generating topic hierarchy for text segments
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Taxonomy generation for text segments: A practical web-based approach
ACM Transactions on Information Systems (TOIS)
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Labeling Nodes of Automatically Generated Taxonomy for Multi-type Relational Datasets
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Document Clustering Description Extraction and Its Application
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Choosing your own adventure: automatic taxonomy generation to permit many paths
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Improving hierarchical document cluster labels through candidate term selection
Intelligent Decision Technologies
Mining semantic relations between research areas
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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Text Mining is an active area of research and development, which combines and expands techniques found in related areas like information retrieval, computational linguistics, and data mining to perform an analysis of large corpora of digital documents. This paper describes the TaxGen Text Mining project carried out at the IBM Software Development Lab. at Boeblingen, Germany. The goal of TaxGen was the automatic generation of a taxonomy for a collection of previously unstructured documents, namely a set of 73.000 news wire documents spanning one year.