An empirical convolutional neural network approach for semantic relation classification
- 1 May 2016
- journal article
- Published by Elsevier BV in Neurocomputing
- Vol. 190, 1-9
- https://doi.org/10.1016/j.neucom.2015.12.091
Abstract
No abstract availableKeywords
Funding Information
- Ministry of Education of the People's Republic of China (20130005110004)
- Higher Education Discipline Innovation Project (B08004)
- National Natural Science Foundation of China (61273217, 61300080)
This publication has 23 references indexed in Scilit:
- Big Data for Modern Industry: Challenges and Trends [Point of View]Proceedings of the IEEE, 2015
- Performance Monitoring for Vehicle Suspension System via Fuzzy Positivistic C-Means Clustering Based on Accelerometer MeasurementsIEEE/ASME Transactions on Mechatronics, 2014
- Improved PLS Focused on Key-Performance-Indicator-Related Fault DiagnosisIEEE Transactions on Industrial Electronics, 2014
- A Neural Network for Factoid Question Answering over ParagraphsPublished by Association for Computational Linguistics (ACL) ,2014
- A Convolutional Neural Network for Modelling SentencesPublished by Association for Computational Linguistics (ACL) ,2014
- #TagSpace: Semantic Embeddings from HashtagsPublished by Association for Computational Linguistics (ACL) ,2014
- Convolutional Neural Networks for Sentence ClassificationPublished by Association for Computational Linguistics (ACL) ,2014
- Semantic Parsing for Single-Relation Question AnsweringPublished by Association for Computational Linguistics (ACL) ,2014
- How many hidden layers and nodes?International Journal of Remote Sensing, 2009
- RelEx—Relation extraction using dependency parse treesBioinformatics, 2006