Deep Sequence Labelling Model for Information Extraction in Micro Learning Service
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Ghassan Beydoun | Li Li | Jun Shen | Geng Sun | David Pritchard | Zhexuan Zhou | Jiayin Lin | Tingru Cui | Dongming Xu | Dongming Xu | G. Beydoun | Zhexuan Zhou | Tingru Cui | Jiayin Lin | Jun Shen | David Pritchard | Li Li | G. Sun
[1] Venkatesh Saligrama,et al. Zero-Shot Learning via Semantic Similarity Embedding , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[2] Jun Shen,et al. A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER , 2019, 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD).
[3] Eduard H. Hovy,et al. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF , 2016, ACL.
[4] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[5] Yue Zhang,et al. Design Challenges and Misconceptions in Neural Sequence Labeling , 2018, COLING.
[6] Haim Eshach,et al. Bridging In-school and Out-of-school Learning: Formal, Non-Formal, and Informal Education , 2007 .
[7] Yunming Ye,et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.
[8] Karin M. Verspoor,et al. Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition , 2018, Louhi@EMNLP.
[9] Weihua Li,et al. Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.
[10] Yang Liu,et al. Bilateral neural embedding for collaborative filtering-based multimedia recommendation , 2017, Multimedia Tools and Applications.
[11] Iasonas Kokkinos,et al. Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Wenpeng Yin,et al. Comparative Study of CNN and RNN for Natural Language Processing , 2017, ArXiv.
[13] Jie Lu,et al. A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System , 2014, IEEE Transactions on Fuzzy Systems.
[14] Dongming Xu,et al. Towards massive data and sparse data in adaptive micro open educational resource recommendation: a study on semantic knowledge base construction and cold start problem , 2017 .
[15] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[16] Bhaskar Mitra,et al. Neural Text Embeddings for Information Retrieval , 2017, WSDM.
[17] Wei Xu,et al. Bidirectional LSTM-CRF Models for Sequence Tagging , 2015, ArXiv.
[18] Wei-Ying Ma,et al. 2D Conditional Random Fields for Web information extraction , 2005, ICML.
[19] Lei Lin,et al. Music Sequence Prediction with Mixture Hidden Markov Models , 2018, 2019 IEEE International Conference on Big Data (Big Data).
[20] Shiping Chen,et al. MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning , 2018, IEEE Transactions on Services Computing.
[21] Shan Wang,et al. A General Multi-Context Embedding Model for Mining Human Trajectory Data , 2016, IEEE Transactions on Knowledge and Data Engineering.
[22] Fang Dong,et al. (WIP) Evaluation of a Cloud-Based System for Delivering Adaptive Micro Open Education Resource to Fresh Learners , 2018, 2018 IEEE 11th International Conference on Cloud Computing (CLOUD).
[23] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[24] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[25] Noah A. Smith,et al. Transition-Based Dependency Parsing with Stack Long Short-Term Memory , 2015, ACL.
[26] Andrew McCallum,et al. Maximum Entropy Markov Models for Information Extraction and Segmentation , 2000, ICML.