Event recognition in epizootic domains

K-REx Repository

Show simple item record

dc.contributor.author Bujuru, Swathi
dc.date.accessioned 2011-01-10T21:59:30Z
dc.date.available 2011-01-10T21:59:30Z
dc.date.issued 2011-01-10
dc.identifier.uri http://hdl.handle.net/2097/7070
dc.description.abstract In addition to named entities such as persons, locations, organizations, and quantities which convey factual information, there are other entities and attributes that relate identifiable objects in the text and can provide valuable additional information. In the field of epizootics, these include specific properties of diseases such as their name, location, species affected, and current confirmation status. These are important for compiling the spatial and temporal statistics and other information needed to track diseases, leading to applications such as detection and prevention of bioterrorism. Toward this objective, we present a system (Rule Based Event Extraction System in Epizootic Domains) that can be used for extracting the infectious disease outbreaks from the unstructured data automatically by using the concept of pattern matching. In addition to extracting events, the components of this system can help provide structured and summarized data that can be used to differentiate confirmed events from suspected events, answer questions regarding when and where the disease was prevalent develop a model for predicting future disease outbreaks, and support visualization using interfaces such as Google Maps. While developing this system, we consider the research issues that include document relevance classification, entity extraction, recognizing the outbreak events in the disease domain and to support the visualization for events. We present a sentence-based event extraction approach for extracting the outbreak events from epizootic domain that has tasks such as extracting the events such as the disease name, location, species, confirmation status, and date; classifying the events into two categories of confirmation status- confirmed or suspected. The present approach shows how confirmation status is important in extracting the disease based events from unstructured data and a pyramid approach using reference summaries is used for evaluating the extracted events. en_US
dc.description.sponsorship Department of Defense en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Information extraction en_US
dc.subject Text analytics en_US
dc.subject Natural language understanding en_US
dc.subject Event recognition en_US
dc.subject Veterinary epidemiology en_US
dc.subject Data mining en_US
dc.title Event recognition in epizootic domains en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor William H. Hsu en_US
dc.subject.umi Agriculture, Animal Pathology (0476) en_US
dc.subject.umi Artificial Intelligence (0800) en_US
dc.subject.umi Computer Science (0984) en_US
dc.date.published 2010 en_US
dc.date.graduationmonth December en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search K-REx

Advanced Search


My Account


Center for the

Advancement of Digital


118 Hale Library

Manhattan KS 66506

(785) 532-7444