Constructing Task-Specific Taxonomies for Document Collection Browsing

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1278–1289, Jeju Island, Korea, 12–14 July 2012. c⃝2012 Association for Computational Linguistics Constructing Task-Specific Taxonomies for Document Collection Browsing Hui Yang Department of Computer Science Georgetown University 37th and O street, NW Washington, DC, 20057 huiyang@cs.georgetown.edu Abstract 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. 1 Introduction Taxonomies are widely used for knowledge stan- dardization, knowledge sharing, and inferencing in natural language processing (NLP) tasks (Harabagiu et al., 2003; Szpektor et al., 2004). However, an- other common function of taxonomies, browsing, has received little attention in the NLP community. Browsing is the task of exploring and accessing in- formation through a structure, e.g. a hierarchy, built upon a given document collection. In fact, tax- onomies serve as browsing tools in many venues, including the Library of Congress Subject Headings (LCSH, 2011) for the U.S. Library of Congress and the Open Directory Project (ODP, 2011) for about 5% of the entire Web. We call taxonomies support- ing browsing as browsing taxonomies. When used for browsing, concepts1 in taxonomies are linked to documents containing them and taxo- nomic structures are navigated to find particular doc- uments. Users can navigate through a browsing tax- onomy to explore the documents in the collection. A browsing taxonomy benefits information access by providing corpus overview for a document col- lection and allowing more focused reading by pre- senting together documents about the same concept. Most existing browsing taxonomies, such as LCSH and ODP, are manually constructed to sup- port large collections in general domains. Not only their constructions are expensive and slow, but also their structures are static and difficult to adapt to spe- cific tasks. In situations where document collections are given ad-hoc, such as search result organization (Carpineto et al., 2009), email collection exploration (Yang and Callan, 2008), and literature investigation (Chau et al., 2011), existing taxonomies may even not be able to provide the right coverage of concepts. It is necessary to explore ad-hoc (semi-)automatic techniques to quickly derive task-specific browsing taxonomies for arbitrary document collections. (Hovy, 2002) pointed out that one key challenge in taxonomy construction is multiple perspectives embedded in concepts and relations. One cause for multiple perspectives is the inherent facets in con- cepts, e.g., jewelries can be organized by price or by gemstone types. Another cause is task specification or even personalization. …

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