New publications on extracting events and sentiment for financial news

Two recent publications on fine-grained information extraction in company-specific news using deep learning.

I am happy to announce two new open access publications in the SENTiVENT project marking the end of my PhD and culminating in my dissertation. [Read More]

CLIN31: Join our session on COVID19 Stance and Opinion and poster on financial polar facts.

Chairing session and poster on economic NLP research.

Our research group organizes the 31st edition of Computational Linguistics in the Netherlands (CLIN31) this year. Join me as I chair the session on COVID-19: Stance and Opinion in Room B to get the latest research on sentiment analysis, stance and topic detection related to the coronavirus pandemic. [Read More]

Extracting Fine-Grained Economic Events from Business News.

New publication and presentation in economic NLP research.

I will be presenting new work on economic event extraction at the FNP-FNS workshop @ COLING2020. TL;DR: We created a dataset and system that automatically gets “what, who, when, how much, and why” is happening from economic news text. Turns out this is quite a difficult task, even with using... [Read More]

Publication: Automatic classification of participant roles in cyberbullying

New publication of cyberbullying research in journal Natural Language Engineering.

Successful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of the event nor... [Read More]

Publication: Current limitations in cyberbullying detection

New publication of cyberbullying research in journal Language Resources and Evaluation.

We provide a cross-domain evaluation framework for cyberbullying detection research to overcome the limitations of data scarcity in this field. We also show that crowdsourcing data in a simulated lab setting is a valid way of generating data. We have published new research as an extension from the AMiCA project... [Read More]