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Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classification are dependent on the choice of hand-crafted features and demand a clear awareness of prior domain knowledge. The feature learning approach may detect efficiently visual characteristics of different modalities, but it is limited to the number of training datasets. To overcome the absence of labeled data, on the one hand, we take deep convolutional neural networks (VGGNet, ResNet) with different depths pre-trained on ImageNet, fix most of the earlier layers to reserve generic features of natural images, and only train their higher-level portion on ImageCLEF to learn domain-specific features of medical figures. Then, we train from scratch deep CNNs with only six weight layers to capture more domain-specific features. On the other hand, we employ two data augmentation methods to help CNNs to give the full scope to their potential characterizing image modality features. The final prediction is given by our voting system based on the outputs of three CNNs. After evaluating our proposed model on the subfigure classification task in ImageCLEF2015 and ImageCLEF2016, we obtain new, state-of-the-art results—76.87% in ImageCLEF2015 and 87.37% in ImageCLEF2016—which imply that CNNs, based on our proposed transfer learning methods and data augmentation skills, can identify more efficiently modalities of medical images.

Cross-domain sentiment classification trains robust classifiers across domains with the help of source domain labeled data. Sentiment is expressed differently in different domains. Sentiment terms that occur in a source domain may not appear in a different target domain. Such feature mismatches hinder cross-domain sentiment classification. Previous studies have addressed this problem by constructing a common feature representation or subspace; however, they have not considered semantic correlations between features. In this paper, we propose a cross-domain semantic correlation auto-correspondence method (CSCW) by capturing similar semantic features from different domains. First, our method uses sentiment-invariance words as features by considering their properties as strong sentiment indicators and their invariance across domains. Second, we extracted the top-N pivot features using a common frequency among source and target domains. These pivot features can then be employed to find semantically similar sentiment features from both domains. Third, for each pivot feature, by calculating the semantic similarity between non-pivot features and pivot features from either domain with the help of Word2Vec, we construct similar-pivot feature pairs that express similar sentiments but in different representations in either domain. Finally, we transform these pairs to align similar sentiment feature representations. This process avoids feature mismatches and reduces sentiment discrepancies between domains. The experimental results from testing on 12 source-target domain pairs of Amazon product reviews demonstrate that our method significantly outperforms previous approaches in sentiment classification. [ABSTRACT FROM AUTHOR]