Friday, May 25, 2018
Wednesday, May 23, 2018
Lecture 23 (24/05/2018): issues in WSD; the knowledge acquisition bottleneck; sense distribution learning
Sunday, May 20, 2018
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.
Friday, May 18, 2018
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.
Friday, May 11, 2018
Lecture 20 (11/05/2018): the attention mechanism with TensorFlow; introduction to Word Sense Disambiguation
Thursday, May 10, 2018
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.
Introduction to BabelNet (http://babelnet.org): multilingual synsets, resources integrated, accuracy, applications. Semantic vector representations: SensEmbed.
Friday, May 4, 2018
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.
Saturday, April 28, 2018
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.
Transition-based dependency parsing with buffer and stack; main transition rules and their implementation with BiLSTMs. Introduction to semantics.