
Contents
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2.1 Introduction 2.1 Introduction
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2.2 Morphology: An Overview 2.2 Morphology: An Overview
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2.3 Morphological Structure and Ambiguity 2.3 Morphological Structure and Ambiguity
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2.4 Computational Morphology 2.4 Computational Morphology
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2.5 Finite-State Morphology 2.5 Finite-State Morphology
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2.5.1 Handling Morphotactics 2.5.1 Handling Morphotactics
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2.5.2 Handling Morphographemics 2.5.2 Handling Morphographemics
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2.5.3 Two-Level Morphology 2.5.3 Two-Level Morphology
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2.5.4 Cascaded Rules 2.5.4 Cascaded Rules
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2.6 Machine Learning and Computational Morphology 2.6 Machine Learning and Computational Morphology
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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References References
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2 Morphology
Get accessKemal Oflazer received his PhD in Computer Science from Carnegie Mellon University. He is currently a faculty member in the CS programme at Carnegie Mellon University’s Doha, Qatar campus. He has served or is serving on the editorial boards of Computational Linguistics, Journal of AI Research, Machine Translation, Natural Language Engineering, and Language Resources and Evaluation. He has worked extensively on developing NLP techniques and resources for Turkish. His current research interests are in using NLP techniques for educational support through Q/A of student questions in a course support setting and generating questions for comprehension testing.
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Published:06 July 2017
Cite
Abstract
Morphology is the study of the structure of words and how words are forme3d by combining smaller units of linguistic information called morphemes. Any natural language processing application will need to computationally process the words in a language before any of the more complex processing is done. This is especially a must for morphologically complex languages. After a compact overview of the basic concepts in morphology, this chapter presents the state-of-the-art computational approaches to morphology, concentrating on two-level morphology and cascaded-rules and describing how morphographemics and morphotactics are handled in a finite-state setting. The chapter then summarizes recent approaches to how machine learning techniques are applied in morphological processing.
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