Paper: Constructing Task-Specific Taxonomies for Document Collection Browsing

ACL ID D12-1117
Title Constructing Task-Specific Taxonomies for Document Collection Browsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Taxonomies can serve as browsing tools for document collections. However, given an ar- bitrary 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 tax- onomies supporting browsing in arbitrary document collections. The supervised ap- proach directly learns semantic distances from users to propose meaningful task-specific tax- onomies. The approach aims to produce glob- ally optimized taxonomy structures by incor- porating path consistency control and user- generated 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 ef- fective.