Date of Degree
Computer Sciences | Linguistics
Event Ordering, Temporal Knowledge Base Population, Temporal Relation Extraction, Temporal Slot Filling
Temporal Information Extraction (TIE) from text plays an important role in many Natural Language Processing and Database applications. Many features of the world are time-dependent, and rich temporal knowledge is required for a more complete and precise understanding of the world. In this thesis we address aspects of two core tasks in TIE. First, we provide a new corpus of labeled temporal relations between events and temporal expressions, dense enough to facilitate a change in research directions from relation classification to identification, and present a system designed to address corresponding new challenges. Second, we implement a novel approach for the discovery and aggregation of temporal information about entity-centric fluent relations.
Cassidy, Taylor, "Temporal Information Extraction and Knowledge Base Population" (2014). CUNY Academic Works.