- 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
At the end of this learning unit, the student is able to :
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:
- Various levels of linguistic analysis
- (Automated) corpus processing: formating, tokenization, data tagging
- Probabilistic language models: N-grams, HMMs
- Part-of-Speech Tagging
- (Probabilistic) Context-Free Grammars: parameter estimation and parsing algorithms
- Introduction to Machine Translation
- Introduction to Deep Learning
- Typical linguistic applications such as automated completion, POS taggers, parsing or machine translation.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Practical projects implemented in Python.
Practical projects are submitted on line and evaluated on the Inginious platform.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.The projects are worth 30 % of the final grade, 70 % for the final exam (closed-book).
The projects cannot be implemented again in second session.
The project grades are fixed at the end of the semester and included as such in the global score for the second session.
The final exam is, by default, a written exam (on paper or, when appropriate, on a computer).
These evaluation rules are subject to possible updates due to the sanitary situation. In particular, the relative weights between the projects and the final exam could be adapted. Such possible updates would be notified to the students by a general announcement posted on the Moodle site of this course.
- Les supports obligatoires sont constitués de l'ensemble des documents (transparents des cours magistraux, énoncés des travaux pratiques, compléments, ...) disponibles depuis le site Moodle du cours.
- Required teaching material include all documents (lecture slides, project assignments, complements, ...) available from the Moodle website for this course.
The material for this final exam is the same as in the normal situation (see "supports de cours").
The global grade for the course is based on the projects implemented during the semester (50 %) + on the individual final exam (50 %).
The projects cannot be re-implemented for the second session. Hence, the project grade is fixed at the end of the semester.