  Swarm Intelligence algorithms have gained significant attention in recent
years as a means of solving complex and non-deterministic problems. These
algorithms are inspired by the collective behavior of natural creatures, and
they simulate this behavior to develop intelligent agents for computational
tasks. One such algorithm is Ant Colony Optimization (ACO), which is inspired
by the foraging behavior of ants and their pheromone laying mechanism. ACO is
used for solving difficult problems that are discrete and combinatorial in
nature. Part-of-Speech (POS) tagging is a fundamental task in natural language
processing that aims to assign a part-of-speech role to each word in a
sentence. In this research paper, proposed a high-performance POS-tagging
method based on ACO called ACO-tagger. This method achieved a high accuracy
rate of 96.867%, outperforming several state-of-the-art methods. The proposed
method is fast and efficient, making it a viable option for practical
applications.
