|
K-State Research Exchange >
K-State Electronic Theses, Dissertations, and Reports >
K-State Electronic Theses, Dissertations, and Reports: 2004 - >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/2097/4593
|
| Title: | Entity extraction, animal disease-related event recognition and classification from web |
| Authors: | Volkova, Svitlana |
| Publication Date: | 2010 |
| Graduation Month: | August |
| Type: | Thesis |
| Degree: | Master of Science |
| Department: | Department of Computing and Information Sciences |
| Major Professor: | William H. Hsu |
| Keywords: | entity extraction
event recognition and classification
web mining
document classification
named entity recognition |
| Abstract: | Global epidemic surveillance is an essential task for national biosecurity management
and bioterrorism prevention. The main goal is to protect the public from major health
threads. To perform this task effectively one requires reliable, timely and accurate medical
information from a wide range of sources. Towards this goal, we present a framework for epidemiological
analytics that can be used to extract and visualize infectious disease outbreaks
from the variety of unstructured web sources automatically. More precisely, in this thesis, we
consider several research tasks including document relevance classification, entity extraction
and animal disease-related event recognition in the veterinary epidemiology domain. First,
we crawl web sources and classify collected documents by topical relevance using supervised
learning algorithms. Next, we propose a novel approach for automated ontology construction
in the veterinary medicine domain. Our approach is based on semantic relationship
discovery using syntactic patterns. We then apply our automatically-constructed ontology
for the domain-specific entity extraction task. Moreover, we compare our ontology-based
entity extraction results with an alternative sequence labeling approach. We introduce a
sequence labeling method for the entity tagging that relies on syntactic feature extraction
using a sliding window. Finally, we present our novel sentence-based event recognition
approach that includes three main steps: entity extraction of animal diseases, species, locations,
dates and the confirmation status n-grams; event-related sentence classification into
two categories - suspected or confirmed; automated event tuple generation and aggregation.
We show that our document relevance classification results as well as entity extraction
and disease-related event recognition results are significantly better compared to the results
reported by other animal disease surveillance systems. |
| Appears in Collections: | K-State Electronic Theses, Dissertations, and Reports: 2004 -
|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
|
