Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Finding topic words for hierarchical summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Open-domain textual question answering techniques
Natural Language Engineering
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning semantic constraints for the automatic discovery of part-whole relations
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A novel word clustering algorithm based on latent semantic analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Ontology generation for large email collections
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Mobile information retrieval with search results clustering: Prototypes and evaluations
Journal of the American Society for Information Science and Technology
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
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
A semi-supervised method to learn and construct taxonomies using the web
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Apolo: making sense of large network data by combining rich user interaction and machine learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Increasing stability of result organization for session search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
| Hi-index | 0.00 |
Taxonomies can serve as browsing tools for document collections. However, given an arbitrary collection, pre-constructed taxonomies could not easily adapt to the specific topic/task present in the collection. This paper explores techniques to quickly derive task-specific taxonomies supporting browsing in arbitrary document collections. The supervised approach directly learns semantic distances from users to propose meaningful task-specific taxonomies. The approach aims to produce globally optimized taxonomy structures by incorporating path consistency control and usergenerated task specification into the general learning framework. A comparison to state-of-the-art systems and a user study jointly demonstrate that our techniques are highly effective.