Foundations of Statistical Natural Language Processing

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Publisher ‏ : ‎ The MIT Press; 1st edition (June 18, 1999)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 620 pages
ISBN-10 ‏ : ‎ 0262133601
ISBN-13 ‏ : ‎ 978-0262133609
Reading age ‏ : ‎ 18 years and up
Grade level ‏ : ‎ 12 and up
Item Weight ‏ : ‎ 2.89 pounds
Dimensions ‏ : ‎ 9.46 x 8.04 x 1.25 inches

Foundations of Statistical Natural Language Processing

Statistical Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that focuses on the development of algorithms and models to process and understand human language. It combines techniques from computer science, machine learning, and linguistics to enable computers to analyze and generate human language text.

The foundations of Statistical NLP lie in the following key concepts:

1. Probability theory: Statistical NLP relies heavily on the principles of probability theory to model uncertainties and make predictions about language data. Techniques such as n-gram models, hidden Markov models, and probabilistic context-free grammars are commonly used to estimate the likelihood of different sequences of words in a given language.

2. Machine learning: Machine learning algorithms play a crucial role in Statistical NLP by enabling computers to learn patterns and relationships in language data. Supervised learning techniques, such as support vector machines and neural networks, are commonly used for tasks such as text classification, named entity recognition, and sentiment analysis.

3. Linguistic theory: Linguistic theories provide a theoretical framework for understanding the structure and meaning of human language. Concepts such as syntax, semantics, and pragmatics are essential for developing NLP models that can accurately analyze and generate natural language text.

4. Computational linguistics: Computational linguistics is the interdisciplinary field that combines linguistics and computer science to study the computational aspects of human language. Techniques such as part-of-speech tagging, parsing, and machine translation are essential for building NLP systems that can effectively process and understand language data.

Overall, the foundations of Statistical NLP encompass a diverse range of techniques and theories from mathematics, computer science, and linguistics. By combining these disciplines, researchers and practitioners can develop advanced NLP systems that can perform a wide range of tasks, from language translation to sentiment analysis, with a high degree of accuracy and efficiency.
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