III: Small: Collaborative Research: Scalable Schema-Based Event Extraction

Suny At Stony Brook, Stony Brook NY

Investigators

Abstract

One of the major bottlenecks in current language understanding algorithms is the lack of commonsense knowledge about how the world works. When we communicate through language, we implicitly assume that the readers will use this common sense knowledge and make the necessary inferences. Computers, on the other hand, do not have access to this shared common knowledge, and as a result are often unable to understand text well enough to perform important tasks such as question answering. This project will study methods to learn one type of common sense knowledge about event scenarios: the series of events (actions) and the types of entities involved. For example, an arrest scenario typically involves a crime event, and an arrest event, with an arresting agent (say police), a suspect, and possibly a victim of the crime. Language understanding algorithms need to be explicitly told to look for these specific types of events and entities. This approach does not scale to the many possible real world event scenarios. This project will develop machine learning algorithms that automatically acquire this type of knowledge covering a broad range of domains in large text collections. Such algorithms can form the basis of a wide variety of assistive technology that enables public access to information. Examples include the generation of schemas from historical documents to assist students in targeted learning about historical events, or extraction of events and actors involved in current world events from streaming news sources. More generally, access to the event structure of documents will enable better question answering capabilities that, embedded appropriately into search engines, can lead to a more informed public. The project will pursue three central research thrusts to learning commonsense event schemas. The first thrust develops new probabilistic algorithms for inducing event schemas that represent real-world scenarios (e.g., a Suspect is arrested by Police, pleads to a Judge, and is later convicted). The second thrust will develop new models that extract instances of these learned schemas from text (e.g., John is the Suspect). This project is unique to previous work by formalizing these as separate tasks, and thus enabling deeper research into knowledge learning apart from traditional relation extraction. Finally, the third thrust will standardize potential evaluation frameworks for event schema research. Due to the young nature of this research area, formal evaluation and analysis is inconsistent across previous work. This project will produce the largest and most diverse set of event schemas through crowd-sourcing, enabling consistent and clear evaluation of future models.