Genetic approach for arabic part of speech tagging
This work addresses part-of-speech tagging for Arabic, a morphologically rich language, but is incremental as it applies an existing genetic algorithm method to this specific domain.
The authors tackled Arabic part-of-speech tagging, a challenge due to the language's morphological richness, by proposing and implementing a genetic algorithm-based tagger, achieving accuracy comparable to other probabilistic approaches.
With the growing number of textual resources available, the ability to understand them becomes critical. An essential first step in understanding these sources is the ability to identify the part of speech in each sentence. Arabic is a morphologically rich language, wich presents a challenge for part of speech tagging. In this paper, our goal is to propose, improve and implement a part of speech tagger based on a genetic alorithm. The accuracy obtained with this method is comparable to that of other probabilistic approaches.