Speaker: Chris Tanner, Lecturer, Institute for Applied Computational Science at Harvard University
While natural language processing (NLP) has experienced enormous progress in recent years, some tasks remain incredibly challenging. Namely, Coreference Resolution is a fundamental, unsolved task that attempts to resolve which words in a body of a text all refer to the same underlying "thing" (e.g., entity or event). This serves as an essential component of many other core NLP tasks, including information extraction, question-answering, document summarization, etc. However, decades of research have primarily focused on resolving entities (e.g., people, locations, organizations), with significantly less attention given to events -- the actions performed. In our work, we developed a state-of-the-art model for event coreference that uses almost no features. Last, we touch on remaining challenges and future directions.
2 окт 2024
