Event Nugget Detection using Thresholding and Classification Techniques
| dc.contributor.author | Satyapanich, Taneeya | |
| dc.contributor.author | Finin, Tim | |
| dc.date.accessioned | 2018-10-17T16:54:18Z | |
| dc.date.available | 2018-10-17T16:54:18Z | |
| dc.date.issued | 2016-11-14 | |
| dc.description | Proceedings of the 9th Text Analysis Workshop | en |
| dc.description.abstract | This paper describes the Event Nugget Detection system that we submitted to the TAC KBP 2016 Event Track. We sent out two runs; UMBC1 and UMBC2. UMBC1 is a sentence-level classification system based on Convolution Neural Network and applied the probability to select a word as an event nugget. UMBC2 is the classification model trained from our features using Weka and filtered out low confidence prediction output using threshold. Our performance was low; we got F1 measure of 34.14 for UMBC1 and 35.24 for UMBC2. | en |
| dc.description.uri | https://tac.nist.gov/publications/2015/participant.papers/TAC2015.UMBC.proceedings.pdf | en |
| dc.format.extent | 5 pages | en |
| dc.genre | conference paper | en |
| dc.identifier | doi:10.13016/M29C6S476 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11584 | |
| dc.language.iso | en | en |
| dc.publisher | National Institute of Standards and Technology | en |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | Event Nugget Detection | en |
| dc.subject | UMBC2 | en |
| dc.subject | probability | en |
| dc.subject | Convolution Neural Network | en |
| dc.subject | UMBC Ebiquity Research Group | en |
| dc.title | Event Nugget Detection using Thresholding and Classification Techniques | en |
| dc.type | Text | en |