Natural Language Processing and Text-Mining - 4GMC14C1

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.