Part-of-Speech Tagging in Persian Language using Convolutional Neural Network

Part-of-speech tagging involves identifying the grammatical roles of words within a sentence, such as nouns, verbs, and objects. This process plays a critical role in a variety of natural language processing (NLP) applications, including machine translation, syntactic parsing, spell-checking, and information retrieval. While significant research has been conducted on part-of-speech tagging for many languages, researchers working with Persian encounter unique challenges due to the language's distinctive syntactic and morphological features. Persian is an inflectional language with a complex system of verb conjugations, noun declensions, and word order variations, making it more difficult to apply standard part-of-speech tagging techniques. Traditional methods have utilized a combination of linguistic and statistical models to address these issues, but achieving high accuracy remains a complex task. In this study, we propose the use of a Convolutional Neural Network (CNN) for part-of-speech tagging in Persian. CNNs have demonstrated significant success in various NLP tasks due to their ability to automatically learn feature representations from raw input data, making them particularly effective for language processing tasks that involve complex patterns. The proposed model was evaluated on a large Persian corpus, and the results show that the CNN-based approach achieves a high accuracy rate of ۹۸.۵۵%. This performance indicates the potential of deep learning techniques, specifically CNNs, in overcoming the challenges associated with Persian part-of-speech tagging. The results suggest that CNNs can effectively capture the intricate syntactic and morphological patterns of Persian, providing a reliable method for part-of-speech tagging that can be further extended to other languages with similar complexities.

Keywords:

Part-of-Speech Tagging , word embedding , Natural Language Processing , text corpus