Sign up
Forgot password?
FAQ: Login

Jurafsky D., Martin J.H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

  • pdf file
  • size 27,06 MB
  • added by
  • info modified
Jurafsky D., Martin J.H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Second edition. — Pearson Prentice Hall, 2009. — 988 p.
With contents and search through the book.
Speech and Language Processing is a general textbook on natural language processing,
with an excellent coverage of the area and an unusually broad scope of topics. It includes
statistical and symbolic approaches to NLP, as well as the main methods of speech
processing.
Regular Expressions and Automata
Words and Transducers
N-grams
Part-of-Speech Tagging
Hidden Markov and Maximum Entropy Models
Phonetics
Speech Synthesis
Automatic Speech Recognition
Speech Recognition: Advanced Topics
Computational Phonology
Formal Grammars of English
Syntactic Parsing
Statistical Parsing
Features and Unification
Language and Complexity
The Representation of Meaning
Computational Semantics
Lexical Semantics
Computational Lexical Semantics
Computational Discourse
Information Extraction
Question Answering and Summarization
Dialog and Conversational Agents
Machine Translation
  • Sign up or login using form at top of the page to download this file.
  • Sign up

See also

Details

Bird S., Klein E., Loper E. Natural Language Processing with Python

  • pdf
O’Reilly, 2009, -504 p. This is a book about Natural Language Processing. By natural language we mean a language that is used for everyday communication by humans; languages such as English, Hindi, or Portuguese. In contrast to artificial languages such as programming languages and mathematical notations, natural languages have evolved as they pass from generation to...
  • 4,10 MB
  • added
  • info modified
Details

Clark A., Fox C., Lappin S. (Eds.) The Handbook of Computational Linguistics and Natural Language Processing

  • pdf
Wiley-Blackwell, 2010 — 800 p. ISBN10: 1118347188 This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major...
  • 2,99 MB
  • added
  • info modified
Details

Goldberg Y., Hirst G. (ed.) Neural Network Methods in Natural Language Processing

  • pdf
Morgan & Claypool Publishers, 2017. — 310 p. — ISBN: 978-1627052986. Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with...
  • 4,32 MB
  • added
  • info modified
Details

Goodfellow Ian, Bengio Yoshua, Courville Aaron. Deep Learning Book

  • pdf
MIT Press, 2016. — 802 p. A comprehensive introduction to neural networks and deep learning by leading researchers of this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers. This is a PDF compilation of online book (www.deeplearningbook.org) Who Should Read This Book? Historical...
  • 22,29 MB
  • added
  • info modified
Details

Manning Christopher D., Schütze Hinrich. Foundations of Statistical Natural Language Processing

  • pdf
Second printing with corrections. — The MIT Press, 2000. — 680 p. — ISBN: 0262133601. Mathematical foundations. Linguistic essentials. Corpus-based work. Collocations. Statistical inference: n-gram models over sparse data. Word sense disambiguation. Lexical acquisition. Markov models. Part-of-speech tagging. Probabilistic context free grammars. Probabilistic parsing....
  • 7,38 MB
  • added
  • info modified
Details

Murphy K.P. Machine Learning: A Probabilistic Perspective

  • pdf
Massachusetts Institute of Technology, 2012. — 1067 p. ISBN: 0262018020, 978-0262018029. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and...
  • 25,69 MB
  • added
  • info modified
Up