Lecture 24 (25/05/2018): semantic roles and semantic role labeling

From word to sentence representations. Semantic roles. Resources: PropBank, VerbNet, FrameNet. Semantic Role Labeling (SRL): traditional features. State-of-the-art neural approaches.

Lecture 23 (24/05/2018): issues in WSD; the knowledge acquisition bottleneck; sense distribution learning

Issues in Word Sense Disambiguation. Addressing the knowledge acquisition bottleneck for improving lexical semantic tasks. Learning sense distributions. 

Lecture 22 (18/05/2018): unsupervised and knowledge-based WSD

Unsupervised Word Sense Disambiguation: Word Sense Induction. Context-based clustering. Co-occurrence graphs: curvature clustering, HyperLex. Knowledge-based Word Sense Disambiguation. The Lesk and Extended Lesk algorithm. Structural approaches: similarity measures and graph algorithms. Conceptual density. Structural Semantic Interconnections. Evaluation: precision, recall, F1, accuracy. Baselines. Entity Linking.

Lecture 21 (17/05/2018): supervised Word Sense Disambiguation

Two important dimensions: supervision and knowledge. Supervised Word Sense Disambiguation: pros and cons. Vector representation of context. Main supervised disambiguation paradigms: decision trees, neural networks, instance-based learning, Support Vector Machines, IMS with embeddings, neural approaches to WSD.

Lecture 20 (11/05/2018): the attention mechanism with TensorFlow; introduction to Word Sense Disambiguation

Hands-on: the attention mechanism with TensorFlow. Introduction to Word Sense Disambiguation.

Lecture 19 (10/05/2018): semantic vector representations (2)

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 18 (04/05/2018): BabelNet; semantic vector representations (1)

Introduction to BabelNet (http://babelnet.org): multilingual synsets, resources integrated, accuracy, applications. Semantic vector representations: SensEmbed.

Lecture 17 (03/05/2018): computational lexicons; WordNet; introduction to WSD; homework 2

Encoding word senses: paper dictionaries, thesauri, machine-readable dictionary, computational lexicons. WordNet. Introduction to Word Sense Disambiguation (WSD). Homework 2: supervised and knowledge-based Word Sense Disambiguation.