Using genetic algorithm for Persian grammar induction
Mohsen Arabsorkhi, Hesham Faili, Mansoor Zolghadri Jahroumi · 2009
Most of efficient computational approaches in NLP tasks are supervised methods which need annotated corpora. But the lack of supervised data in Persian encourages researchers to increase their interests and efforts on unsupervised and semi-supervised approaches. This paper presents a novel semi-supervised approach which called Genetic-based inside-outside (GIO), for Persian grammar inference for inducing a grammar model in a PCFG formalism. GIO is an extension of the inside-outside algorithm enriched by some notions of genetic algorithm. In pure genetic algorithm for grammar induction, randomly generated initial population make it computationally expensive, so we used inside-outside algorithm to generate initial population. Our experiments show that our approach's result is better than other applied methods for Persian grammar induction.