التفاصيل البيبلوغرافية
| العنوان: |
Exploring Multiple Embedded Features on Event Extraction |
| المؤلفون: |
Yi, Shi-Xiang, Li, Chun-Yan |
| المصدر: |
Journal of Physics: Conference Series ; volume 1267, issue 1, page 012033 ; ISSN 1742-6588 1742-6596 |
| بيانات النشر: |
IOP Publishing |
| سنة النشر: |
2019 |
| الوصف: |
In recent years, the neural network method can automatically learn effectively features. Unlike traditional discrete features, neural network features are mostly continuous features and can be automatically combined to build higher-level features. The efficiency of the features has been proven in numerous tasks in natural language processing and has led to breakthroughs. In this paper, we propose a event extraction system based on combination of multiple embedded features. Our work is mainly based on the three aspects: (1) traditional pipeline systems have serious error propagation problems; (2) there are several different event descriptions in the text; (3) representation learning can provide rich semantic and syntactic representation. As a result, we achieve competitive performance, specifically, F1-measure of 60.25 in event extraction. Meanwhile, evaluation results point out some shortcomings that need to be addressed in future work. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| DOI: |
10.1088/1742-6596/1267/1/012033 |
| DOI: |
10.1088/1742-6596/1267/1/012033/pdf |
| الإتاحة: |
https://doi.org/10.1088/1742-6596/1267/1/012033Test |
| حقوق: |
http://creativecommons.org/licenses/by/3.0Test/ ; https://iopscience.iop.org/info/page/text-and-data-miningTest |
| رقم الانضمام: |
edsbas.1B77A3AF |
| قاعدة البيانات: |
BASE |
