23 July 2016
Introduction to Natural Language Processing
Sequential models (Hidden Markov Models, Conditional Random Fields)
Shallow syntactic parsing
Deep syntactic parsing
Knowledge representation (taxonomies, componential semantics, frame semantics, supersenses)
Text classification (supervised, weakly supervised, semi-supervised, and unsupervised)
Python libraries (we will not be looking at all of these in detail but during the course we will be borrowing methods and classes from all of them):
gensim (word2vec, topic modelling)
Raw text corpora (provided by lecturer)
Full list to be confirmed soon
Annotated corpora (provided by lecturer)
Crowdflower’s public sentiment analysis dataset
PoS-tagged annotated corpus
Good level of English.
Familiarity with mathematical notation and scientific formalization.
Familiarity with basic probability theory and Bayesian statistics.
Familiarity with basic concepts of information retrieval (precision and recall).
Strong problem formalization skills, particularly probabilistic factorization (for instance, as applied to a company’s expected sales volume: “if each unit sells for x euro, and y units are sold in a given period, if some ratio r1 of all units sold are returned, and if some ratio r2 are damaged, and if the actually sold items result in an average ratio r3 euro of additional sales per quarter, and if the bad reviews from damaged items result in an average ratio of r4 lost sales per quarter, what is the total expected profit for a quarter where 1,000 units were sold?”
Mr. Jordi Carrera Ventura
Computational Linguist from Barcelona with many years of experience working on industrial NLP applications
Affiliation: Quarizmi AdTech / AAA Group / Sumplify, Catalonia, Spain
I started my career working on automatic know
This course is targeted to bachelors, masters, PhD students, lecturers and specialists in Computer Science Bioinformatics and Computational Biology, Statistics, Mathematics, Electrical and Computer Engineering, Chemical and Biological Engineering, Industrial Engineering, Material Science and Engineering, Neuroscience, Human-Computer Interaction, Psychology, Business and related discipines
This course is a part of Lviv Computer Science Summer School (whole school's credit cost is 3 ects)This course is a part of Lviv Computer Science Summer School (whole school's credit cost is 3 ects)
EUR 300: This course is a part of Lviv Computer Science Summer School (whole school's cost is 300 EUR)
EUR 350: This course is a part of Lviv Computer Science Summer School (whole school's cost is 350 EUR)