The course targets text analytics systems and applications to respond to business problems by discovering and presenting knowledge that is otherwise locked in textual form. The main objectives of the course are:
|Date||Lecture||Slides||Material / Reference|
|2022/09/16||Introduction to the course, NLP & Text Analytics.||1 - Introduction to the Text Analytics course||J. Eisenstein. Introduction to Natural Language Processing. MIT Press. Chp. 1.|
|2022/09/19||Reminds on Probability. Language and Probability.||2 - Reminds on Probability.pdf|
|2022/09/23||Introduction to Python||3 - Introduction to Python.pdf||Introduction to Python - Notebook|
|2022/09/30||Introduction to Python (continued). Project Presentation and Important Dates.||Project and Dates|
The exam for attending students will consist of the development of a project to be agreed upon with the teacher and an oral exam. The outcome of the project will be some code and a report of the activity (4-10 pages is the typical length range). The oral exam will consist of the presentation and discussion of the project. Projects may be based on challenges proposed in either research forums (Semeval, Evalita) or other platforms (Kaggle). Students are also invited to propose a project based on other sources (e.g., recent papers on ArXiv CL or AI), or their own interests. Students may work in 3-5 people groups.
The exam for non attending students will consist in a written exam with open question and exercises, and an oral discussion on the topics of the course.
It is recommended to read selected chapters from:
Further bibliography will be indicated as a material for the single lessons.