@Inproceedings {export:230414,
abstract     = {<p>We investigate the problem of entity ranking towards descriptive queries, that
                aims to match entities referred in user queries to entities of a large knowledge
                base (KB). Entity ranking faces the primary challenge of the sparseness of entity
                related data, such as various ways of referring to an entity. The lack of
                sufficient variations of entity referring expressions in KB makes it difficult to
                find entities referred in user queries, especially when the queries are
                descriptive. We tackle this problem by enriching KB entries using web documents
                and query click logs. First, we propose a novel method of injecting textual
                information from web documents to the KB on a large scale. Since the number of
                web documents can be large, we propose to use keyword extraction and
                summarization techniques for compactly representing entity-related information.
                Second, we mine web search query logs to link entities to existing queries.
                Experiments show significant improvements after the KB enrichment, compared with
                two competitive baselines. We also achieve further improvements by combining the
                data from these two resources.</p>},
author       = {Kai Hong and Pengjun Pei and Ye-Yi Wang and Dilek Hakkani-Tur},
booktitle    = {Proceedings of Spoken Language Technology Workshop},
month        = {December},
publisher    = {IEEE – Institute of Electrical and Electronics Engineers},
title        = {Entity Ranking for Descriptive Queries},
url          = {http://research.microsoft.com/apps/pubs/default.aspx?id=230414},
year         = {2014},
}