We seek to push the boundaries in several areas of Natural Language Processing (NLP) by performing both basic research and solving applied problems, partially in cooperation with industry partners.

Natural Language Processing is a cross-disciplinary research field that draws heavily from artificial intelligence (AI), machine learning (ML), mathematics, and linguistics. AI drives the current, fast-paced evolution of language technology. Better capabilities to process natural language are much needed, as humanity’s data production has never been higher. Most of this data is in the form of text (e.g., tweets, blog posts, search queries, and code). Personal assistants, recommender systems, fake news identification, financial stock analysis, chatbots, autocorrection, auto-completion, intelligent search engines, and automatic translation or captioning are just a few examples of how NLP and AI are helping us to manage the flood of data. However, systems to process natural language are far from perfect, which leaves much space for research.

Enabling machines to understand and generate natural language is a challenging task. Natural language is full of nuances and implicit elements that sometimes are hard to grasp, even for humans. However, this makes the problems computers need to solve even more exciting. We believe NLP and AI play a fundamental role in helping humans achieve great things. Therefore, we focus our research on foundational aspects of NLP and solving complex use-case-specific challenges.

Check out our detailed list of ongoing projects here.

Some areas we are working in are:

Basic Research Applications
Language Modeling Paraphrase Detection
Natural Language Understanding Media Bias Analysis
Semantic Feature Extraction Fake News Detection
Text Classification Trend/News Pattern Identification
Text Summarization/Generation Identification of toxic individuals
Word Sense Disambiguation Low-resource Language Applications
Sentiment Analysis Literature Review Automation
CoReference Resolution Explainable NLP/AI
Meta-analysis of NLP research Ethics, bias, and broader impact of NLP