Number of hours
- Lectures 7.5
- Projects -
- Tutorials 7.5
- Internship -
- Laboratory works -
- Written tests 1.0
ECTS
ECTS 1.5
Goal(s)
The course introduces the first tools for processing textual data.
Students will learn how to manipulate textual data in order to prepare it for analysis.
The analysis methods that will be learned allow students to match strings and vocabulary; to explore texts and identify their characteristics (mutual information, similar or complementary information); to classify texts and identify typologies.
Content(s)
1. Introduction to NLP (Natural Language Processing) and TM (Text-Mining) (1 C-TD)
2. Pre-processing (2 C-TD): Parsing, tokenization, case folding, lemmatisation, stemming, POS-tagging, sentence splitting, stop words removal…
3. Text representations (1 C-TD): Vector Space Model, Bag-of-words model, TF, TF-IDF, Word2vec, GLOVE…
4. Feature selection (1 C-TD): X², mutual information, information gain…
5. Text classification (2 C-TD): One VS. Multi class, Bias VS. Variance, Kappa test, training set, validation set, testing set, accuracy, validation set, leave-one-out cross-validation, K-fold cross validation, precision, recall, F-Measure, confusion matrix…
Students will have taken and validated the following courses: Probability and Statistics; Programming with R
the following weighting of grades is compatible with distance exams
In-class exam (at least 2 grades TP1 and TP2
Individual final exam grade: E1
Grade = 0.4*((TP1+TP2)/2) + 0.6*E1
Cette pondération est compatible avec une organisation des enseignements et des examens en distanciel
N1 = E1
N2 = E2
The course exists in the following branches:
- Curriculum - Engineer student Master SCM - Semester 7
- Curriculum - Engineer student Master PD - Semester 7
Course ID : 4GMC14C1
Course language(s):
You can find this course among all other courses.
Manning, Christopher D., and Hinrich Schutze. 1999. Foundations of Statistical Natural Language Processing. The MIT Press. Cambridge, Mass.: MIT Press.
Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda. 2018. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning (1st. ed.). O'Reilly Media, Inc.
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, USA.
French State controlled diploma conferring a Master's degree
Common Core presentation
Programme courses S5
Programme courses S6
Supply Chain Management
Programme presentation
Programme courses S7
Programme courses S8
Programme courses S9
Programme courses S10
Product Design
Programme presentation
Programme courses S7
Programme courses S8
Programme courses S9
Programme courses S10
Contacts
Head of studies: Pierre Lemaire
Head of 1st Year Program: Abdourahim Sylla
Head of Supply Chain Management Program: Irène Gannaz
Head of Product design Program: Guillaume Thomann
Registrar's office
Head of Registrar's office: genie-industriel.scolarite@grenoble-inp.fr
Secretary's office 1st Year: Valérie Demicheli
Secretary's office 2nd Year: Sylvie Malandrino
Secretary's office 3rd Year: Vincente Odier
International relations department:
Nadia Dehemchi