Lecture 16 (28/04/2020, Google meet, 2 hours): 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. Lexical knowledge resources: WordNet.

Lecture 15 (23/04/2020, Google meet, 3 hours): neural syntactic parsing

Graph-based and transition-based neural syntactic parsing. Deep biaffine attention for neural syntactic parsing. 

 

Lecture 14 (21/04/2020, Google meet, 2 hours): more on syntactic parsing

Syntactic parsing: top-down and bottom-up. Structural ambiguity.  Backtracking vs. dynamic programming for parsing. The CKY algorithm.

Lecture 13 (16/04/2020, Google meet, 3 hours): language modeling hands-on and intro to syntactic parsing

Neural Language modeling with LSTMs. Neural network tricks: sentence packing; weight tying; custom dropout. Introduction to syntax. Context-free grammars and languages. Treebanks. Normal forms. Dependency grammars.

Lecture 12 (07/04/2020, Google meet, 2 hrs): LSTMs and character-embeddings

Gated architectures, Long-Short Term Memory networks (LSTMs). Bidirectional LSTMs and stacked LSTMs. How to create an LSTM with PyTorch. Part-of-speech tagging with LSTMs. Character embeddings.

Lecture 11 (02/04/2020, Google meet, 3 hrs): RNNs, Part-of-speech tagging (3/3)

Word2vec and word embedding properties and regularities. Identifying and managing multiword expressions. Working with sequences: Recurrent Neural Networks (RNNs). More on POS tagging with RNNs. The CoNLL format. Homework assignment: Named Entity Recognition.