An Entropy-Based Approach for Preserving Diversity in Evolutionary Topical Search

BAGGIO, MARIA CECILIA; CECCHINI, ROCÍO LUJÁN; LORENZETTI, CARLOS MARTÍN; MAGUITMAN, ANA GABRIELA

Buenos Aires

Congreso; Simposio Argentino de Inteligencia Artificial 2016 -- 45 JAIIO; 2016

SADIO y Universidad de Tres de Febrero

Topic-based information retrieval is the process of matching a topic ofinterest against the resources that are indexed. An approach for retrieving topic relevant resources is to generate queries that are able to reflect the topic of interest.Multi-objective Evolutionary Algorithms have demonstrated great potentialto 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 oflabeled documents show the effectiveness of the proposed strategy.