Our faculty is developing data-driven methods on mining structured facts (i.e., entities and their relations for types of interest) from massive text corpora to construct knowledge bases, with a focus on methods that are minimally supervised, domain-independent, and language-independent for timely knowledge base construction across various application domains (e.g., news, social media, biomedical, business). The research is investigating the querying large-scale knowledge bases in a schema-agnostic manner, using ideas such as keyword query, graph query, natural language query, and query by example.
The UC Santa Barbara Natural Language Processing Group studies the theoretical foundation and practical algorithms for language technologies. It tackles challenging learning and reasoning problems under uncertainty, and pursue answers via studies of machine learning, deep learning, and interdisciplinary data science. The lab concentrates in the areas of information extraction, computational social science, knowledge graph, learning to reason, dialogue systems, language & vision, summarization, statistical relational learning, reinforcement learning, structure learning, and deep learning.
The Center For Information and Technology (CITS) is dedicated to research and education about the cultural transitions and social innovations associated with technology, particularly in the highly dynamic environments that seem so pervasive in organizations and societies today. It also works to improve engineering through infusing social insights into the innovative process. Faculty associated with the Center bring their diverse disciplinary perspectives—which range from Art and English to Sociology and Communication to Computer Science and Electrical Engineering—into conversation, forwarding cutting edge research across the engineering sciences, the social sciences, and the humanities. Affiliated faculty have, naturally, pursued research to assess the impact of big data and analytical techniques on the way people live and work.