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}}