Expansion finding for given acronyms using conditional random fields
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Learning conditional random fields with latent sparse features for acronym expansion finding
Proceedings of the 20th ACM international conference on Information and knowledge management
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Recently, combining Conditional Random Fields (CRF) with Neural Network has shown the success of learning high-level features in sequence labeling tasks. However, such models are difficult to train because of the increase of the parameters to tune which needs enormous of labeled data to avoid over fitting. In this paper, we propose a transfer learning framework for the sequence labeling task of gesture recognition. Taking advantage of the frame correlation, we design an unsupervised sequence model as a pseudo auxiliary task to capture the underlying information from both the labeled and unlabeled data. The knowledge learnt by the auxiliary task can be transferred to the main task of CRF with a deep architecture by sharing the hidden layers, which is very helpful for learning meaningful representation and reducing the need of labeled data. We evaluate our model under 3 gesture recognition datasets. The experimental results of both supervised learning and semi-supervised learning show that the proposed model improves the performance of the CRF with Neural Network and other baseline models.