Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Morphological Analyzer for Maithili using Machine Learning

Morphological Analyzer for Maithili using Machine Learning

Singh, Prabhat Kumar

Paperback

General Computers

ISBN10: 9999329896
ISBN13: 9789999329897
Publisher: Eliva Press
Published: Dec 5 2025
Pages: 150
Weight: 0.46
Height: 0.32 Width: 6.00 Depth: 9.00
Language: English
I n the ever-expanding landscape of Natural Language Processing (NLP), the ability to dissect and understand the building blocks of a language is a foundational step. While powerful tools for morphological analysis exist for globally dominant languages like English, a vast number of the world's languages, particularly those with rich oral traditions and distinct linguistic structures, have been left behind in the digital revolution. This is especially true for Maithili, a language spoken by millions across the Mithila region of India and Nepal, yet one that has remained largely underrepresented in the digital sphere. The development of a robust morphological analyzer for Maithili is not just a technological feat; it is a critical step toward preserving and promoting its unique heritage in the modern age. Morphological analysis is the process of breaking down words into their constituent morphemes-the smallest units of meaning. For a language like Maithili, with its complex system of verb conjugations, case markers, and grammatical agreements, this task is particularly challenging. A word like पढैछी (paṛhaichī) must be broken down to its root, पढ (paṛha), meaning to read, and the suffix -ैछी (-aichī), which denotes the first-person singular present tense. Similarly, विद्यार्थीहरूले (vidyārthīharūle) contains the base word विद्यार्थी (vidyārthī) for student, the plural marker -हरू (-harū), and the case marker -ले (-le) that indicates the agent of an action. Accurately parsing these structures is essential for any advanced language processing application. Traditional rule-based approaches, which rely on manually created dictionaries and a fixed set of grammatical rules, often fall short when dealing with Maithili. Its extensive irregularities, nuanced phonetic shifts, and a wide array of dialectal variations make it difficult to create a comprehensive and scalable rule set. Any small change or new word would require a manual update to the system, making it brittle and high-maintenance. This is where the power of machine learning provides a transformative solution.

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