Dr. Ruihong Huang received the National Science Foundation’s Faculty Early Career Development Award for her research focused on extracting events and understanding the relationship between them from natural language texts (news articles, manuscripts, blogs, etc.).
This understanding is the key to carrying out various analytical tasks such as predicting future events, detecting misinformation and other attempts to validate events, managing extreme events, answering complex questions and generating concise text summaries for analysis. These insights will help government entities, companies and the general public with improving situational awareness, reducing information overload and assisting with timely decision-making.
Events described in various natural language texts play a large role in forming a cohesive story, and their presence is tightly related to the overall structure of a document and how it is organized.
With the number of documents describing real-world events growing larger on a daily basis, document-level event graphs, which are models used to filter and structure information about the events described in text, are in high demand.
“During this research I will study correlations between events and the way in which an entire document is organized, in order to overcome the fundamental difficulties in identifying event-event relations posed by the long distance between event mentions and the range of different words used to describe them,” said Huang.