An n-Gram and Initial Description Based Approach for Entity Ranking Track

Murugeshan, Meenakshi Sundaram; Mukherjee, Saswati

doi:10.1007/978-3-540-85902-4_26

Cited by 2 publications

(1 citation statement)

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“…Craswell et al [2009] go beyond this and modify the query with NLP techniques, removing verbs while focussing on adjectives, nouns, and named entities; Murugeshan and Mukherjee [2008] attempt to improve on query modeling by identifying meaningful n-grams in the keyword query.…”

Section: Entity Ranking At Inexmentioning

confidence: 99%

Query modeling for entity search based on terms, categories, and examples

Balog

Bron

Rijke

2011

ACM Trans. Inf. Syst.

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Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.

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Section: Entity Ranking At Inexmentioning

confidence: 99%