9789999329897 - Morphological Analyzer for Maithili using Machine Learning de Singh, Prabhat Kumar; Patidar, Harish (17 resultados)

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Paperback. Condición: new. Paperback. 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 "" (pahaichi) must be broken down to its root, "" (paha), meaning "to read," and the suffix "-" (-aichi), which denotes the first-person singular present tense. Similarly, "L" (vidyarthiharule) contains the base word "" (vidyarthi) for "student," the plural marker "-" (-haru), and the case marker "-L" (-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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condición: new. Paperback. 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 "" (pahaichi) must be broken down to its root, "" (paha), meaning "to read," and the suffix "-" (-aichi), which denotes the first-person singular present tense. Similarly, "L" (vidyarthiharule) contains the base word "" (vidyarthi) for "student," the plural marker "-" (-haru), and the case marker "-L" (-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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. 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 "" (pahaichi) must be broken down to its root, "" (paha), meaning "to read," and the suffix "-" (-aichi), which denotes the first-person singular present tense. Similarly, "L" (vidyarthiharule) contains the base word "" (vidyarthi) for "student," the plural marker "-" (-haru), and the case marker "-L" (-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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. Morphological Analyzer for Maithili using Machine Learning | Prabhat Kumar Singh | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999329897 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.