Thursday, January 17, 2019

Ready, steady, go!

Welcome to the Sapienza NLP course blog! This year there will be important changes:

  1. You will write a paper 
  2. The course will be even more deep learning oriented
  3. For attending students, there will be only three homeworks (and no additional duty), one of which will be done with delivery by the end of September and will replace the project. Non-attending students, instead, will have to work on a full-fledged project.

IMPORTANT: The 2019 class hour schedule will be on Thursday 16.30-19 and Fridays 14.00pm-16.30pm, Aula 2 - Aule L ingegneria.

Please sign up to the NLP class!


Monday, June 4, 2018

Lecture 26 (01/06/2018): neural machine translation + end of the course!

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 25 (31/05/2018): bilingual/multilingual embeddings; semantic parsing

Bilingual and multilingual embeddings. Offline vs. online embeddings. 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.


Friday, May 25, 2018

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.


Wednesday, May 23, 2018

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


Sunday, May 20, 2018

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.


Friday, May 18, 2018

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.

Thursday, May 10, 2018

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.