Abstract
In this paper, we propose an attention-based multi-task neural network model for text classification and sequence tagging and then apply it to the named entity recognition and the intent analysis of Chinese online medical questions. We found that the use of both attention and multi-task learning improved the performance of these tasks. Our method achieved superior performance in named entity recognition and intent analysis compared with other baseline methods; the method is a light-weight solution that is suitable for deployment on small servers. Furthermore, we took advantage of the model's capabilities for these two tasks and built a simple question-answering system for cardiovascular issues. Users and service providers can monitor the logic of the answers generated by this system.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
