Date of Degree

6-2014

Document Type

Dissertation

Degree Name

Ph.D.

Program

Linguistics

Advisor

Heng Ji

Subject Categories

Computer Sciences | Linguistics

Keywords

Event Ordering, Temporal Knowledge Base Population, Temporal Relation Extraction, Temporal Slot Filling

Abstract

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.

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