Capabilities and Limitations of Persian Stemming in Natural Language Processing

This article presents a review of stemming techniques for the Persian language, encompassing structural methods, statistical approaches, and lookup tables. In addition, we explore the potential improvement of Persian stemming by drawing insights from theoretical research and experimental results on languages sharing common challenges with Persian. Through a meticulous analysis, we propose the incorporation of Byte Pair Encoding (BPE) and Sequence-to-Sequence (Seq2Seq) models into the Persian stemming framework. This recommendation is rooted in the unique strengths of these methods, tailored to address Persian's intricate morphology, extensive loanword integration, and script diversity. BPE excels in capturing prevalent morphemes and managing out-of-vocabulary terms, while Seq2Seq models show promise in decoding implicit morphological rules and accommodating linguistic idiosyncrasies. In light of Persian's status as a low-resource language in need of advanced technological resources, we put forward a novel enhancement for Persian stemming. This enhancement leverages both BPE and Seq2Seq models within a unified NLP pipeline, signifying a promising path for further research in Persian language processing. By harnessing linguistic insights, this approach has the potential to contribute significantly to bridging the digital language divide for Persian.
Introduction
     The realm of Natural Language Processing (NLP) stands at the frontier of innovation, continually propelled by the evolving need to unravel the complexities inherent in language. Within this dynamic landscape, the Persian language emerges as a challenging domain, demanding specialized attention due to its rich morphology, intricate script, diverse word formation processes, and extensive usage of loanwords. A pivotal facet of linguistic processing in this context is stemming, a technique aimed at reducing words to their root or base form, providing the foundation for a spectrum of NLP tasks. Stemming has garnered substantial recognition for enhancing computational efficiency, text analysis, and information retrieval. This study embarks on a meticulous exploration of stemming techniques tailored to the idiosyncrasies of Persian. By examining established structural and statistical methods, as well as dictionary-based approaches, this investigation evaluates their effectiveness and identifies their limitations in the Persian language context. Moreover, the study advocates for an innovative integration of advanced machine learning methods, specifically Sequence-to-Sequence (Seq2Seq) models and Byte Pair Encoding (BPE), to unlock the potential for significantly enhanced Persian stemming. In presenting a roadmap towards bridging the linguistic digital divide, this study aims to invigorate the future landscape of Persian language processing and contribute to the broader advancement of NLP across diverse linguistic domains.
Material and Methods
     The study comprehensively evaluates various stemming methodologies for the Persian language, focusing on their applicability and efficacy. The material utilized for this research primarily includes Persian language datasets, linguistic resources, and NLP tools. The datasets encompass a diverse collection of Persian texts, covering different domains and styles of writing to ensure a broad representation of language usage. Linguistic resources consist of lexicons, grammatical rules, and linguistic databases essential for understanding Persian morphology. In addition, various NLP tools are employed, ranging from structural and statistical stemmers to advanced machine learning models like Byte Pair Encoding (BPE) and Sequence-to-Sequence (Seq2Seq) models. These tools are utilized to analyze and process the datasets, generating insights into the strengths and weaknesses of each stemming technique. The methods involve a systematic evaluation of these tools, considering factors such as accuracy, computational efficiency, and adaptability to the complexities of the Persian language. The study also proposes an innovative approach that integrates BPE and Seq2Seq models, demonstrating a potential enhancement in Persian stemming. The material and methods employed in this research aim to provide a comprehensive understanding of stemming techniques for the Persian language and pave the way for advancements in Persian language processing.
Results and Discussion
     The study yields substantial insights into stemming techniques for the Persian language. Structural stemmers, relying on predefined linguistic rules, demonstrate simplicity and effectiveness but face challenges in handling exceptions, especially within verb conjugations, and in coping with the nuances of the Persian script. Statistical stemmers, driven by probabilistic models and machine learning, display adaptability to linguistic variations and prove efficient in managing irregular forms and homographic affixes inherent in Persian. Despite the need for significant training data, they outperform rule-based counterparts in managing complexities. Lookup tables, leveraging dictionaries, offer an alternative approach, accounting for irregularities and variations in word forms. However, they demand a comprehensive and precise dictionary, presenting a challenge given Persian's extensive vocabulary and the complexity introduced by loanwords. Furthermore, the rich and intricate morphology of Persian, characterized by diverse word formation processes, compounds the challenges of stemming. Several linguistic factors, including compound verbs, diverse plural suffixes, Persian script intricacies, loanwords, and code-switching pose substantial hurdles in morphological analysis. These complexities underline the critical need for advanced approaches such as deep learning techniques, particularly Sequence-to-Sequence (Seq2Seq) models and Byte Pair Encoding (BPE), to enhance stemming accuracy and adaptability. The proposed integration of Seq2Seq and BPE models within a unified NLP pipeline presents a promising avenue for addressing the intricacies of Persian morphology and advancing the field of Persian language processing. Overall, the study underscores the importance of considering language-specific properties and employing advanced methodologies to bridge the digital language divide effectively.
Conclusion
     This comprehensive exploration of stemming techniques for the Persian language sheds light on critical aspects of natural language processing in a linguistically rich and diverse context. The study underscores the significance of stemming as a fundamental pre-processing step, vital for enhancing various NLP applications such as information retrieval, text mining, sentiment analysis, and machine translation. The assessment of traditional structural and statistical stemming methods, alongside dictionary-based approaches, illuminates their strengths and limitations in addressing the intricacies of Persian morphology, script, and vocabulary. These insights guide the proposal of a synergistic integration of advanced methods, particularly Sequence-to-Sequence (Seq2Seq) models and Byte Pair Encoding (BPE), as a potential pathway to significantly enhance Persian stemming. By emphasizing the need for language-specific considerations and advanced computational methodologies, this study contributes to the ongoing efforts to bridge the digital language divide, paving the way for future research in Persian language processing and, by extension, to enriching NLP advancements in diverse linguistic landscapes.
Ethical Considerations
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Funding
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Conflict of interest
The authors declare no conflict of interest.