dc.contributor.author |
Sawaya, Nancy Joseph |
dc.date.accessioned |
2021-09-23T09:00:30Z |
dc.date.available |
2022-09 |
dc.date.available |
2021-09-23T09:00:30Z |
dc.date.issued |
2019 |
dc.date.submitted |
2019 |
dc.identifier.other |
b25782344 |
dc.identifier.uri |
http://hdl.handle.net/10938/23187 |
dc.description |
Thesis. M.S. American University of Beirut. Department of Computer Science, 2019. T:7093. |
dc.description |
Advisor : Dr. Shady Elbassuoni, Assistant Professor, Computer Science ; Members of Committee : Dr. Fatima Abu Salem, Associate Professor, Computer Science ; Dr. Mohamed El Baker Nassar, Assistant Professor, Computer Science. |
dc.description |
Includes bibliographical references (leaves 122-123) |
dc.description.abstract |
An important natural language processing (NLP) task is to extract structured information from free text. In this thesis, we focus on the problem of extracting war incidents from news articles. A war incident is a tuple consisting of a location of the incident, the actor, the cause of death, and the number of casualties. We employ OpenTag [1], a deep sequence-tagging approach, followed by a series of flat classifiers to achieve this task. To train our sequence tagging model and the flat classifiers, we utilize a dataset of news articles surrounding the Syrian war. Our approach, which utilizes sequence tagging, outperforms baseline classifiers that rely solely on the text of the news articles. |
dc.format.extent |
1 online resource (xvii, 123 leaves) : color illustrations |
dc.language.iso |
en |
dc.subject.classification |
T:007093 |
dc.subject.lcsh |
Natural language processing. |
dc.subject.lcsh |
Machine learning. |
dc.subject.lcsh |
Neural networks (Computer science) |
dc.title |
Extracting war incidents from news articles via deep Sequence Tagging |
dc.type |
Thesis |
dc.contributor.department |
Department of Computer Science |
dc.contributor.faculty |
Faculty of Arts and Sciences. |
dc.contributor.institution |
American University of Beirut. |