Advanced learning for text and graph data ALTEGRAD
LearningNatural Language Processing

Objectif du cours

The ALTEGRAD course ( 28 hours) aims at providing an overview of state-of-the-art ML and AI methods for text and graph data with a significant focus on applications. Each session will comprise two hours of lecture followed by two hours of programming sessions.

Grading for the course will be based on a final data challenge plus lab based evaluation.


Inscription to the course necessary : link to form

Course web page: here

Informative video: here

Course Syllabus 2021-2022 :

1.1 TEXT/NLP  Graph based Text Mining
  •  Graph-of-words  GoWvis 
  • Keyword extraction (TFIDF, TextRank, ECIR’15, EMNLP’16)
  •  extractive summarization (EMNLP’17)
  • Sub-event detection in twitter streams (ICWSM’17) 
  • graph based document classification: TW-IDF (ASONAM’15), TW-ICW, subgraphs (ACL’15)
  • abstractive summarization – ACL 2018 summarization

1.2 TEXT  NLP – Word & doc embeddings (P)

  • Word embeddings: word2vec-glove models,  doc2vec, subword, Latent Semantic Indexing, context based embeddings  
  • doc similarity metrics: Word Mover’s distance, shortest path kernels (EMNLP16)

1.3 Deep learning for NLP


Course Syllabus 2020-2021 :

1.4 Graph kernels, community detection

Grakel python library:


1.5 Deep Learning for Graphs – node classification

  • node embeddings (deepwalk & node2vec) for node classification and link prediction  
  • Supervised node embeddings (GCNN, …)

1.6 Deep Learning for Graphs – Graph classification, GNNs

  • graph CNNs
  • message passing
  • Graph – Auto-encoders

1.7 Sets embeddings  point clouds

1.8 Network Architecture Search – interpretability.

Les intervenants

Michalis Vazirgiannis


voir les autres cours du 1er semestre