
Contents
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24.1 Introduction 24.1 Introduction
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24.2 N-gram Models 24.2 N-gram Models
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24.3 Data Sparseness 24.3 Data Sparseness
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24.4 Generative versus Discriminative Tagging Models 24.4 Generative versus Discriminative Tagging Models
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24.4.1 Hidden Markov Models 24.4.1 Hidden Markov Models
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24.4.1.1 HMM parameters 24.4.1.1 HMM parameters
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24.4.1.2 Supervised training 24.4.1.2 Supervised training
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24.4.1.3 Inference to the best tagging solution 24.4.1.3 Inference to the best tagging solution
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24.4.2 Discriminative Models 24.4.2 Discriminative Models
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24.4.2.1 Maximum Entropy models 24.4.2.1 Maximum Entropy models
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24.4.2.2 ME parameters 24.4.2.2 ME parameters
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24.4.2.3 Inference to the best tagging solution 24.4.2.3 Inference to the best tagging solution
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24.4.2.4 Conditional Random Field model 24.4.2.4 Conditional Random Field model
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24.4.2.5 Bidirectional Long Short-Term Memory Deep Neural Network with a CRF layer 24.4.2.5 Bidirectional Long Short-Term Memory Deep Neural Network with a CRF layer
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24.4.3 Rule-Based Methods 24.4.3 Rule-Based Methods
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24.4.3.1 Constraint Grammar tagging 24.4.3.1 Constraint Grammar tagging
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24.4.3.2 Transformation-based tagging 24.4.3.2 Transformation-based tagging
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24.5 Conclusions 24.5 Conclusions
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Further Reading and Relevant Resources Further Reading and Relevant Resources
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References References
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24 Part-of-Speech Tagging
Get accessDan Tufiș is Professor of Computational Linguistics and Director of the Institute of Artificial Intelligence in Bucharest (since 2002). He graduated from the faculty of Computer Science of the ‘Politehnica’ University of Bucharest in 1979, obtaining a PhD from the same university in 1992. His contributions in NLP (paradigmatic morphology, POS tagging, WSD, QA, MT, word alignment, large mono- and multilingual corpora and dictionaries, wordnet, etc.) have been published in more than 300 scientific papers.
Radu Ion is a Senior Researcher at the Research Institute for Artificial Intelligence in Bucharest. He graduated from the Faculty of Computer Science at the Politehnica University of Bucharest in 2001, and received his PhD from the Romanian Academy in 2007. Among his research interests are ML for NLP, NLU, MT, and CL problems such as WSD and dependency parsing. He has co-authored 76 publications in peer-reviewed conferences and journals.
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Published:05 October 2017
Cite
Abstract
One of the fundamental tasks in natural-language processing is the morpho-lexical disambiguation of words occurring in text. Over the last twenty years or so, approaches to part-of-speech tagging based on machine learning techniques have been developed or ported to provide high-accuracy morpho-lexical annotation for an increasing number of languages. Due to recent increases in computing power, together with improvements in tagging technology and the extension of language typologies, part-of-speech tags have become significantly more complex. The need to address multilinguality more directly in the web environment has created a demand for interoperable, harmonized morpho-lexical descriptions across languages. Given the large number of morpho-lexical descriptors for a morphologically complex language, one has to consider ways to avoid the data sparseness threat in standard statistical tagging, yet ensure that full lexicon information is available for each word form in the output. The chapter overviews the current major approaches to part-of-speech tagging.
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