Morphological Analyzer for Maithili using Machine Learning
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Morphological Analyzer for Maithili using Machine Learning
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Description
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 "¿¿¿¿¿" (pähaich¿) must be broken down to its root, "¿¿" (päha), meaning "to read," and the suffix "-¿¿¿" (-aich¿), which denotes the first-person singular present tense. Similarly, "¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿" (vidy¿rth¿har¿le) contains the base word "¿¿¿¿¿¿¿¿¿¿" (vidy¿rth¿) for "student," the plural marker "-¿¿¿" (-har¿), and the case marker "-¿¿" (-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.
Product details
- Type:
- Book
- Binding:
- Paperback
- Languages:
-
Original:
English
- Number of Pages:
-
150
- Publisher:
-
Eliva Press
- Publication Date:
-
2025-12-05
- Release Date:
-
2025-08-07
- ISBN / EAN:
-
9789999329897
- Weight:
-
228 g
- Dimensions:
-
15/23/1 cm
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