RI: Small: Robust Models for Sequence Labelling in Social Media Data

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In the last decade social media platforms have increased their impact on the way people communicate; these platforms are now considered an essential communication tool that people use broadly to share information, but also to get informed about the latest events on any topic. Consequently, the information running through those platforms, generated by users, companies, the media, and political entities, is extremely relevant to understand current events, behaviors, and more, and the automated distillation of this data is of great practical value. Current technology for text processing fails to perform information extraction accurately on social media data since these sophisticated algorithms have been trained on highly edited English text with a narrow set of topics, such as that in newswire data. In contrast, social media data has a fluid grammar, a very large vocabulary, unlimited topics, and includes multiple languages that are often mixed in the same text. This project addresses the many challenges involved in the automated processing from social media sources. Additionally, the research team will develop and release new annotated data that will enable new research in this direction. Furthermore, this project will address broadening participation in computer science by supporting graduate and undergraduate students, several of them from underrepresented groups in Computer Science.The underlying premise of this project is that a tighter coupling of representation learning with linguistic and domain knowledge will allow the models to learn the tasks by distilling all relevant linguistic abstractions in each single text, without requiring prohibitively large amounts of labeled data, as is typically the case in end-to-end deep-learning models. This award will design robust approaches for sequence labeling tasks that can analyze social media data with a two-pronged approach. First, the research team will study the challenges imposed by social media data and their correlation to prediction performance. Then the investigators will design new model architectures for sequence labeling tasks where domain and linguistic knowledge supervise the learning process. The evaluation of the proposed models will include data from different social media sources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.