Natural Language Processing Books, Course Data and Tutorials

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Natural Language Processing Defination
The history of natural language processing generally started in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled ``Computing Machinery and Intelligence`` which proposed what is now called the Turing test as a criterion of intelligence.

The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem. However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted until the late 1980s, when the first statistical machine translation systems were developed.

Course Contents for Natural Language Processings


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Introduction and Overview, Ambiguity and uncertainty in language, Regular Expressions. Chomsky hierarchy, regular languages, and their limitations. Finite-state automata. Practical regular expressions for finding and counting language phenomena. A little morphology. In class demonstrations of exploring a large corpus with regex tools, String Edit Distance and Alignment, Key algorithmic tool: dynamic programming, first a simple example, then its use in optimal alignment of sequences. String edit operations, edit distance, and examples of use in spelling correction, and machine translation, Context-Free Grammars, Constituency, CFG definition, use, and limitations. Chomsky Normal Form. Top-down parsing; bottom-up parsing, and the problems with each. The desirability of combining evidence from both directions, Information Theory, What is information? Measuring it in bits. The “noisy channel model.” The “Shannon game”–motivated by language! Entropy, cross-entropy, information gain. Its application to some language phenomena, Language modeling and Naive Bayes, Probabilistic language modeling and its applications. Markov models. N-grams. Estimating the probability of a word, and smoothing. Generative models of language. Their application to building an automatically-trained email spam filter, and automatically determining the language, Part of Speech Tagging and Hidden Markov Models, The concept of parts-of-speech, examples, usage. The Penn Treebank and Brown Corpus. Probabilistic (weighted) finite state automata. Hidden Markov models (HMMs), definition and use, Probabilistic Context-Free Grammars, Weighted context-free grammars, Maximum Entropy Classifiers, The maximum entropy principle, and its relation to maximum likelihood. The need in NLP to integratemany pieces of weak evidence. Maximum entropy classifiers and their application to document classification, sentence segmentation.

Reference Materials Recommended By HEC

1. Daniel Jurafsky and James H. Martin. 2008. Speech and Language Processing: An Introduction to Natural Language Processing, 43 Computational Linguistics and Speech Recognition. Second Edition. Prentice Hall.
2. Foundations of Statistical Natural Language Processing, Manning and Schütze, MIT Press. Cambridge, MA: May 1999

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