Lecture 16 (27/04/2018): introduction to computational and lexical semantics

Introduction to computational semantics. Syntax-driven semantic analysis. Semantic attachments. First-Order Logic. Lambda notation and lambda calculus for semantic representation. 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 15 (26/04/2018): transition-based syntactic parser; introduction to semantics

Transition-based dependency parsing with buffer and stack; main transition rules and their implementation with BiLSTMs. Introduction to semantics.

Lecture 14 (20/04/2018): syntactic parsing (2/2)

The Early algorithm. Probabilistic CFGs. Probabilistic parsing, Neural transition-based dependency parsing with LSTMs: arc-factored dependency parsing.

Lecture 13 (19/4/2018): syntactic parsing (1/2)

Introduction to syntax. Context-free grammars and languages. Treebanks. Normal forms. Dependency grammars. Syntactic parsing: top-down and bottom-up. Structural ambiguity.  Backtracking vs. dynamic programming for parsing. The CKY algorithm.

Lecture 12 (13/04/2018): practical session on BiLSTMs with TensorFlow

Practical session on BiLSTMs with TensorFlow.

Lecture 11 (12/04/2018): character embeddings; multilingual POS tagging with word and character embeddings; computational syntax

Character embeddings; multilingual POS tagging with word and character embeddings; introduction to syntax. Context-free grammars and languages. Treebanks.

  

Lecture 10 (06/04/2018): Recurrent Neural Networks and LSTM; POS tagging with LSTMs in TensorFlow

Introduction to Recurrent Neural Networks (RNNs): definitions and configurations. Simple RNN, CBOW as RNN, gated architectures, Long-Short Term Memory networks (LSTMs). Neural POS tagging with LSTMs in TensorFlow.

Lecture 9 (05/04/2018): part-of-speech tagging

Stochastic part-of-speech tagging. Hidden markov models. Deleted interpolation. Linear and logistic regression: Maximum Entropy models. Transformation-based POS tagging. Handling out-of-vocabulary words. The Stanford POS tagger.