Applications in Computational Linguistics
- To introduce the students with a few computational linguistics applications on social media texts.
- To introduce the students to resources and tools for natural language processing in Python such as NLTK and sklearn.
- Student knows how to identify demographic characteristics of bloggers’ texts authors, is able to distinguish between human and bot
- Student can conduct sentiment analysis and detect irony
- Student is able to detect fake news and hate speech
- Student knows and is able to apply main approaches to identify misleading content
- Student is able to detect plagiarism in texts
- Topic 1. Introduction to computational linguistics. Author profiling in social media.
- Topic 2. Opinion mining and irony detection
- Topic 3. Fake news and hate speech detection
- Topic 4. Social media misuse: fake reviews and paedophile
- Topic 5. Text re-use and plagiarism detection
- ExamThe exam is conducted in a form of competition between groups of students, within which the students must build a set of machine learning algorithms based on a test dataset with sentiment markup. A set of algorithms means combinations of different classifiers, neural models, or machine learning algorithms. Students choose a set of algorithms independently. The constructed sets of algorithms must be tested on a test collection during the competition. The results should be presented in the form of a paper draft and a Power Point presentation.
- Mitkov R. (ed.). The Oxford handbook of computational linguistics. – Oxford University Press, 2005.
- Bird, S., Loper, E., & Klein, E. (2009). Natural Language Processing with Python. O’Reilly Media.
- Pozzi F. et. al. Sentiment Analysis in Social Networks. - Morgan Kaufmann Publishers, 2016. - ЭБС Books 24x7.