CLiPS, University of Antwerp
The Computational Linguistics & Psycholinguistics Research Group of the University of Antwerp (CLiPS, http://www.clips.uantwerpen.be) focuses on applications of statistical and machine learning methods, trained on corpus data, to explain human language acquisition and processing data, and to develop automatic text analysis systems that are accurate, efficient, and robust enough to be used in practical applications.
There are 3 subgroups to CLiPS: (1) the sociolinguistics group studies language variation in different demographic groups. The (2) psycholinguistics group studies the effect of cochlear implantation on child language acquisition. This description focuses on (3) the computational linguistics group.
Current research at CLiPS' Computational Linguistics Group focuses on developing tools that can extract data from social media messages, such as fine-grained sentiment analysis, and the detection of subversive behavior on social network sites (sexually transgressive behavior, hate speech, ...). Furthermore, CLiPS is well known for its work on computational stylometry and has developed state-of-the-art technology for authorship attribution, as well as author profiling, i.e. the detection of personality, age and gender of the author of a text, based on personal writing style. Another line of research at CLiPS focuses on computational psycholinguistics and researches psychologically plausible models of child language acquisition and bilinguality. CLiPS also researches and develops tools for biomedical text mining.
Over the years, CLiPS has established a strong reputation in the application of machine learning methods on a variety of language technology problems for a wide range of languages. To capitalize on this reputation, a spin-off company, Textgain (textgain.com), was founded in 2015 that aims to bring CLiPS technology to the market for commercial purposes.
CLiPS, University of Antwerp 2018 Projects
Multi Pronged Approach to Text AnonymizationText Anonymization refers to the processing of text, stripping it of any attributes/identifiers thus hiding sensitive details and protecting the...
Pattern 3 Natural Language processingThe idea of the project is to improve the Pattern framework and complete some useful tasks. It's important also to add new functionality and modern...
Seed, Context-Free Generation and Twitter BotsThis project explores the topics of context-free generation, topic analysis and sentiment analysis. It focuses on work with Seed (an open-source...
Sentiment analysis of figurative language in political tweetsI aim to develop a sentiment analysis sub-tool for classifying the polarity of political tweets containing figurative language (most likely idioms...