Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

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.