Computational Linguistics [ LINGI2263 ]

• algorithmics and preferably basic knowledge in machine learning (as provided by SINF1121 and ING2262)

• 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

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.