AI634 | Natural Language Processing
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Course overview

Understanding the human language by machines is one of the important topics in computer science. There is a large range of tools and technologies for natural language processing that are used by many users in daily life: from the simplest cases such as spell checkers and grammar checkers to more complicated systems such as speech recognition, machine translation, question answering, email categorization, handwriting recognition, and search engines.

What you will learn
Course program
In this course, the main techniques and applications of natural language processing will be introduced. In addition, we briefly describe language modeling and machine learning concepts that are required to deal with language processing techniques and applications.
Lessons Name Description Duration Week Slides
1 Introduction to NLP NLP Applications and Techniques 3 hours 1 NLPF2101
2 Linguistic Jnowledge Semantics, Pragmatics, Discourse, Ambiguity, Phonetics and Phonology 3 hours 2 NLPF2102
3 Regular Expressions Named Entity Recognition, Information Extraction, Chatter Bot 3 hours 3 NLPF2103
4 Finite State Automata D-Recognize Algorithm, Formal Language, DFSA, NFSA, State-Speech Search 3 hours 4 NLPF2104
5 Morphology Morphology Trees, Morphology Parsing, Stemming, Lemmatization, Finite State Lexicon 3 hours 5 NLPF2105
6 Finite State Transducers FST for Morphological Parsing, Ortographical Rules, WordNet, BioLemmatizer, CRF, LSTM 3 hours 6 NLPF2106
7 Basic Text Processing Text Normalization, Corpus Datasheets, Tokenization, NLTK 3 hours 7 NLPF2107
8 Byte Pair Encoding Token Learner Algorithm, Token Segmenter, Word Normalization, Porter Stemmer 3 hours 8 NLPF2108
9 Minimum Edit Distance The Alignment Game, Longest Common Subsequence, Edit Distance in NLP 6 hours 9,10 NLPF2109
10 Back-Trace for Computing Alignments Optimal Alignment, Back Tracking, Weighted Edit Distance, Computational Biology 3 hours 11 NLPF2110
11 Language Modelling Markov Assumption, Unigram, Bigram, N-Gram, Estimating Probabilities, Google N-Gram 3 hours 12 NLPF2111
12 Evaluation and Perplexity Evaluation of N-Gram Model, Shannon Game, Shakespear as Corpus, Overfitting 3 hours 13 NLPF2112
13 Smmothing Add-one (Laplace) Smoothing, Interpolation, Backoff, Web-Scale LMs, Kneser-Ney Smoothing 6 hours 14,15 NLPF2113