Computational Linguistics [ LINGI2263 ]

> https://www.icampus.ucl.ac.be/claroline/course/index.php?cid=INGI2263

• Basics in phonology, morphology, syntax and semantics
• Linguistic resources
• Part-of-speech tagging
• Statistical language modeling (N-grams and Hidden Markov Models)
• Robust parsing techniques, probabilistic context-free grammars
• Linguistics engineering applications such as spell or syntax checking software, POS tagging, document indexing and retrieval, text categorization

Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

• INFO1.1-3
• INFO2.3-4
• INFO5.3-5
• INFO6.1, INFO6.4

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

• SINF1.M4
• SINF2.3-4
• SINF5.3-5
• SINF6.1, SINF6.4

Students completing successfully this course should be able to

• describe the fundamental concepts of natural language modeling
• master the methodology of using linguistic resources (corpora, dictionaries, semantic networks, etc) and make an argued choice between various linguistic resources
• apply in a relevant way statistical language modeling techniques
• develop linguistic engineering applications

Students will have developed skills and operational methodology. In particular, they have developed their ability to

• integrate a multidisciplinary approach to the edge between computer science and linguistics, using wisely the terminology and tools of one or the other discipline,
• manage the time available to complete mini-projects,
• manipulate and exploit large amounts of data.

25% for practical works + 75% final exam (closed book)

No possibility to present again practical works in the second session

• 12 lectures
• 3 miniprojects
• feedback sessions about the miniprojects

• Linguistic essentials: morphology, part-of-speech, phrase structure, semantics and pragmatics
• Mathematical foundations: formal languages, and elements of information theory
• Corpus analysis: formating, tokenization, morphology, data tagging
• N-grams: maximum likelihood estimation and smoothing
• Hidden Markov Models: definitions, Baum-Welch and Viterbi algorithms
• Part-of-Speech Tagging
• Probabilistic Context-Free Grammars: parameter estimation and parsing algorithms, tree banks
• Machine Translation: classical and statistical methods (IBM models, Phrase-based models), evaluation
• Applications: SMS predictors, POS taggers, information extraction

Required slides available at:
http://www.icampus.ucl.ac.be/claroline/course/index.php?cid=INGI2263

1 textbook recommended:

• Speech and Language Processing (2nd Edition), D. Jurafsky and J.H. Martin, Prentice Hall, 2009.

Background:

• LSINF1121: Algorithmics and data structures