Descriptif
Processing language is one of the most important and most challenging issues of Artificial Intelligence. NLP (Natural Language Processing) has many applications. It is commonly used in machine translation, in text mining, in speech recognition, in dialogue based applications, in text generation, in automatic summarization, in Web search, etc. Conversely, it is hard to imagine an “intelligent” machine that would be unable to understand language.
NLP remains a challenging task. Statistical techniques perform well in domains such as machine translation, but they are intrinsically limited to average meanings and cannot take contextual knowledge into account. This course explores some symbolic alternatives to mere statistics.
Some NLP techniques, like grammar and parsing and ontologies, are classic symbolic methods. Some others are inspired by cognitive modelling. They include procedural semantics, aspect processing, dialogue processing. The point is not only to adopt a “reverse engineering” approach to language, but also to adapt engineering techniques to human requirements to improve efficiency and acceptability.
Objectifs pédagogiques
Processing language is one of the most important and most challenging issues of Artificial Intelligence. NLP (Natural Language Processing) has many applications. It is commonly used in machine translation, in text mining, in speech recognition, in dialogue based applications, in text generation, in automatic summarization, in Web search, etc. Conversely, it is hard to imagine an “intelligent” machine that would be unable to understand language.
NLP remains a challenging task. Statistical techniques perform well in domains such as machine translation, but they are intrinsically limited to average meanings and cannot take contextual knowledge into account. This course explores some symbolic alternatives to mere statistics.
Some NLP techniques, like grammar and parsing and ontologies, are classic symbolic methods. Some others are inspired by cognitive modelling. They include procedural semantics, aspect processing, dialogue processing. The point is not only to adopt a “reverse engineering” approach to language, but also to adapt engineering techniques to human requirements to improve efficiency and acceptability.
effectifs minimal / maximal:
12/Diplôme(s) concerné(s)
Parcours de rattachement
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Master M2 - Data & Artificial Intelligence
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Echange international non diplomant
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Master M1 - Data and Artificial Intelligence
Pour les étudiants du diplôme Diplôme d'ingénieur
Vos modalités d'acquisition :
- Answers to questions during lab sessions will be recorded. They will be evaluated when students who are close to failing based on other criteria.
- Students will be asked to perform a small technical study by extending some issue addressed during lab sessions. They will be given the opportunity to present their work during a few minutes at the end of the course. They will also write a four-page report.
- Students will answer a small quiz (open questions, no documents).
- Crédits ECTS acquis : 2.5 ECTS
- Crédit d'UE électives acquis : 2.5
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Master M2 - Interaction, Graphic & Design
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS