Evaluation of Different Similarity Measures for the Extraction of Multiword Units in a Reinforcement Learning Environment
GaŰl Dias, SÚrgio Nunes
Universidade da Beira Interior
In this paper, we present an evaluation of four different similarity measures for the extraction of multiword units in the context of the GALEMU software that proposes an innovative architecture based on a floating point representation genetic algorithm. In particular, we will show that the Bray and Curtis measure leads to improved extraction results both in precision and recall.
Multiword Unit Extraction, Genetic Algorithms, GALEMU, Similarity Measures