Topic-based information retrieval is the process of matching a topic of interest against the resources that are indexed. An approach for retrieving topicrelevant resources is to generate queries that are able to reflect the topic of interest.
Multi-objective Evolutionary Algorithms have demonstrated great potential to deal with the problem of topical query generation. In an evolutionary approach to topic-based information retrieval the topic of interest is used to generate an initial population of queries, which is evolved towards successively better candidate queries. A common problem with such an approach is poor recall due to loss of genetic diversity. This work proposes a novel strategy inspired on the information theoretic notion of entropy to favor population diversity with the aim of attaining good global recall. Preliminary experiments conducted on a large dataset of labeled documents show the effectiveness of the proposed strategy.
Información general
Fecha de exposición:septiembre 2016
Fecha de publicación:18 de noviembre de 2016
Idioma del documento:Español
Evento:Simposio Argentino de Inteligencia Artificial (ASAI 2016) - JAIIO 45 (Tres de Febrero, 2016).
Institución de origen:Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)