Temporal Information Extraction and Knowledge Base Population

<p>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. </p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Linguistics

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2014

Author dc:creator

• Cassidy, Taylor

Advisor dc:contributor.advisor

• Heng Ji

• Computer Sciences
• Linguistics
• Event Ordering
• Temporal Knowledge Base Population
• Temporal Relation Extraction
• Temporal Slot Filling

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/185

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-1184