Deep Sequence Labelling Model for Information Extraction in Micro Learning Service
| Autor: | Zhexuan Zhou, Geng Sun, Ghassan Beydoun, Li Li, Jiayin Lin, David E. Pritchard, Dongming Xu, Jun Shen, Tingru Cui |
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| Rok vydání: | 2020 |
| Předmět: |
Conditional random field
Service (systems architecture) Artificial neural network Computer science business.industry Feature extraction 02 engineering and technology 010501 environmental sciences computer.software_genre Machine learning 01 natural sciences Data modeling Information extraction Recurrent neural network 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence Hidden Markov model business computer 0105 earth and related environmental sciences |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9206606 |
| Popis: | Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant pre-processing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services. |
| Databáze: | OpenAIRE |
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