Lecture 17 (29/04/2021, 2 hours): semantic vector representations: SensEmbed and NASARI; linkage to BabelNet

Semantic vector representations: importance of their multilinguality; linkage to BabelNet; latent vs. explicit representations; monolingual vs. multilingual representations. The NASARI lexical, unified and embedded representations.

Lecture 16 (26/04/2021, 3 hours): intro to NER; semantic vector representations; Q&A on homework

Introduction to the Named Entity Recognition task. Semantic vector representations. Q&A on homework 1.

Lecture 15 (22/04/2021, 2 hours): POS tagging and NER with BiLSTMs

 Practical session: POS tagging and Named Entity Recognition with BiLSTMs.

Lecture 14 (19/04/2021, 3 hours): lexical semantics (2/2) and lexical-semantic knowledge resources

Human vs. computer dictionaries. Introduction to WordNet. The notion of synset. Lexical and semantic relations. Multilingual lexical-semantic knowledge graphs. BabelNet: motivation, creation, organization and evolution. What comes first? Language or concept? Two different views.

Lecture 13 (15/04/2021, 15-17, 2 hours): introduction to lexical semantics

Introduction to lexical semantics. Lexicon, lemmas and word forms. Word senses: monosemy vs. polysemy. Special kinds of polysemy. Computational sense representations: enumeration vs. generation. Graded word sense assignment.

Lecture 12 (12/04/2021, 14-17, 3 hours): jump into the future + RNN notebook

Talk on cross-lingual Semantic Role Labeling and Semantic Parsing (Cleopatra workshop at The Web Conference) + RNNs in PyTorch (notebook).

Lecture 11 (08/04/2021, 2.5 hours): Homework 1 presentation + RNNs practical session

Homework 1: the Word-in-Context disambiguation task. Recurrent Neural Networks in practice with PyTorch.

Lecture 10 (29/03/2021, 3.5 hours): more on word2vec, GloVe, RNNs, LSTMs and PyTorch Lightning

More on Word2Vec and word embeddings: hierarchical softmax; negative sampling. GloVe. Recurrent Neural Networks. Gated architectures, Long-Short Term Memory networks (LSTMs). Bidirectional LSTMs and stacked LSTMs. Character embeddings. Introduction to PyTorch Lightning

Lecture 9 (25/03/2021, 2 hours): part-of-speech tagging

Part-of-speech tagging. Hidden markov models. Deleted interpolation. Linear and logistic regression: Maximum Entropy models; logit and logistic function; relationship to sigmoid and softmax. Transformation-based POS tagging. Handling out-of-vocabulary words.