Swarm intelligence for natural language processing
Wojdan Al Saeedan, Mohamed El Bachir Menaï · International Journal of Artificial Intelligence and Soft Computing · 2015
Natural language processing (NLP) is an area dealing with computational methods for achieving human-like language processing. Traditionally, NLP research has been focused on developing efficient and robust algorithms to treat most NLP tasks, including syntactic and semantic analysis, grammar induction, summary and text generation, document clustering and machine translation. Swarm intelligence (SI) methods are effective to do so, since they have been successfully applied for many real-world problems. Recently, NLP and SI have been active areas of research, joined together more than once to solve problems in NLP field. This paper presents a review of recent developments of SI methods in NLP. It shows that only a few NLP tasks and applications were tackled by using SI-based algorithms. These mainly include text document clustering and classification, text summarisation, word sense disambiguation, information retrieval, and speaker recognition. This study also shows that four SI-based algorithms were examined in NLP field, including ant colony optimisation (ACO), particle swarm optimisation (PSO), bee swarm optimisation (BSO), and firefly algorithm (FA), emphasising ACO and PSO as the most investigated algorithms in this field.