Lecture 23 (31/05/2019): semantic role labeling, semantic parsing, machine translation

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

Semantic parsing: definition, comparison to Semantic Role Labeling, approaches, a recent approach in detail. The Abstract Meaning Representation formalism. Introduction to machine translation (MT) and history of MT. Overview of statistical MT. The EM algorithm for word alignment in SMT. Beam search for decoding. Introduction to neural machine translation: the encoder-decoder neural architecture; back translation; byte pair encoding. The BLEU evaluation score. Performances and recent improvements. End of the course!

Lecture 22 (30/05/2019): presentation of research in Rome

Presentation of research carried out in the multilingual NLP research at Sapienza: multilingual Word Sense Disambiguation, semantic role labeling, knowledge acquisition.

Lecture 21 (28/05/2019): prof. Pustejovsky's lecture on "Visualizing Meaning: Semantic Simulation of Actions and Events"

Visualizing Meaning: Semantic Simulation of Actions and Events

Lecture 20 (24/05/2019): neural WSD, unsupervised WSD, knowledge-based WSD

Neural Word Sense Disambiguation. 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 19 (23/05/2019): hierarchical softmax, negative sample, GloVe, intro to WSD

Hierarchical softmax, negative sample, GloVe, intro to WSD.

Lecture 18 (17/05/2019): more on multilingual semantic vector representations. Bilingual and multilingual embeddings. intro to Word Sense Disambiguation

More on semantic vector representations. Bilingual and multilingual embeddings. Introduction to Word Sense Disambiguation.

Lecture 17 (16/05/2019): more on semantic vector representations

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 (09/05/2019): more on BabelNet, intro to semantic vector representations

More on BabelNet. Introduction to semantic vector representations: motivation, examples, un supervised approaches.

Lecture 15 (03/05/2019): lexical knowledge resources (WordNet, BabelNet)

Encoding word senses: paper dictionaries, thesauri, machine-readable dictionary, computational lexicons. WordNet. Brief introduction to BabelNet.

Lecture 14 (02/05/2019): introduction to computational 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.