INEX

A Generative Language Modeling Approach for Ranking Entities

We describe our participation in the INEX 2008 Entity Ranking track. We develop a generative language modeling approach for the entity ranking and list completion tasks. Our framework comprises the following components: (i) entity and (ii) query language models, (iii) entity prior, (iv) the probability of an entity for a given category, and (v) the probability of an entity given another entity. We explore various ways of estimating these components, and report on our results. We find that improving the estimation of these components has very positive effects on performance, yet, there is room for further improvements.

  • [PDF] W. Weerkamp, K. Balog, and E. Meij, “A generative language modeling approach for ranking entities,” in Advances in focused retrieval, 2009.
    [Bibtex]
    @inproceedings{INEX:2008:weerkamp,
    Abstract = {We describe our participation in the INEX 2008 Entity Ranking track. We develop a generative language modeling approach for the entity ranking and list completion tasks. Our framework comprises the following components: (i) entity and (ii) query language models, (iii) entity prior, (iv) the probability of an entity for a given category, and (v) the probability of an entity given another entity. We explore various ways of estimating these components, and report on our results. We find that improving the estimation of these components has very positive effects on performance, yet, there is room for further improvements.},
    Author = {Weerkamp, W. and Balog, K. and Meij, E.},
    Booktitle = {Advances in Focused Retrieval},
    Date-Added = {2011-10-16 12:29:08 +0200},
    Date-Modified = {2011-10-16 12:29:08 +0200},
    Organization = {Springer},
    Publisher = {Springer},
    Title = {A Generative Language Modeling Approach for Ranking Entities},
    Year = {2009}}
Stack of books

Concept models for domain-specific search

We describe our participation in the 2008 CLEF Domain-specific track. We evaluate blind relevance feedback models and concept models on the CLEF domain-specific test collection. Applying relevance modeling techniques is found to have a positive effect on the 2008 topic set, in terms of mean average precision and precision@10. Applying concept models for blind relevance feedback, results in even bigger improvements over a query-likelihood baseline, in terms of mean average precision and early precision.

  • [PDF] E. Meij and M. de Rijke, “Concept models for domain-specific search,” in Evaluating systems for multilingual and multimodal information access, 9th workshop of the cross-language evaluation forum, clef 2008, aarhus, denmark, september 17-19, 2008, revised selected papers, 2009.
    [Bibtex]
    @inproceedings{CLEF:2008:meij,
    Author = {Meij, Edgar and de Rijke, Maarten},
    Booktitle = {Evaluating Systems for Multilingual and Multimodal Information Access, 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, September 17-19, 2008, Revised Selected Papers},
    Date-Added = {2011-10-12 18:31:55 +0200},
    Date-Modified = {2012-10-30 08:44:35 +0000},
    Title = {Concept models for domain-specific search},
    Year = {2009}}