Recent advances in deep neural networks (DNNs) have enabled us to achieve reliable named entity recognition (NER) models without handcrafting features. However, these are also some obstacles imposed by using those machine learning methods, in need of a large amount of manually labeled data. To avoid such limitations, we could replace human annotation with distant supervision, however there remain a technical challenge on the error label issue caused by ignoring the entities that are not included in the vocabulary, which should be addressed to achieve the effective NER model. Then, we propose a novel back-labeling approach and integrate it into a tagging scheme, especially, we apply this scheme to handle the NER task in traditional Chinese medicine (TCM) field. In addition, we discuss how to use distant supervision methods to achieve better performance of the NER model. We conduct some experiments and verify that our scheme can effectively improve the entity recognition on the basis of distant supervision.

Multi-label image classification is more in line with the real-world applications. This problem is difficult due to the the fact that complex label space makes it hard to get label-level attention regions and deal with semantic relationships among labels. Common deep network-based methods utilize CNN to extract features and consider the labels as a sequence or a graph, thus handling the label correlations with RNN or graph-theoretical algorithms. In this paper, we propose a novel CNN-RNN-based model, bi-modal multi-label learning(BMML) framework. Firstly, an improved channel-wise attention mechanism is presented to propose regional attention maps and connect them to relative labels. After that, based on the assumption that objects in a semantic scene always have high-level relevance among visual and textual corpus, we further embed the labels through different pre-trained language models and determine the label sequence in a “semantic space” constructed on large-scale textual data, thereby handling the labels in their semantic context. In addition, a cross-modal feature aligning module is introduced in BMML framework. Experimental results show that BMML is able to achieve better accuracies then those mainstream multi-label classification methods on several benchmark data sets.

Text classification has always been an interesting issue in the research area of natural language processing (NLP). While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN) in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE) and restricted Boltzmann machine (RBM), our proposed method, named denoising deep neural network (DDNN), is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier.

Background: Traditional Chinese medicine (TCM) clinical records contain the symptoms of patients, diagnoses, and subsequent treatment of doctors. These records are important resources for research and analysis of TCM diagnosis knowledge. However, most of TCM clinical records are unstructured text. Therefore, a method to automatically extract medical entities from TCM clinical records is indispensable. Objective: Training a medical entity extracting model needs a large number of annotated corpus. The cost of annotated corpus is very high and there is a lack of gold-standard data sets for supervised learning methods. Therefore, we utilized distantly supervised named entity recognition (NER) to respond to the challenge. Methods: We propose a span-level distantly supervised NER approach to extract TCM medical entity. It utilizes the pretrained language model and a simple multilayer neural network as classifier to detect and classify entity. We also designed a negative sampling strategy for the span-level model. The strategy randomly selects negative samples in every epoch and filters the possible false-negative samples periodically. It reduces the bad influence from the false-negative samples. Results: We compare our methods with other baseline methods to illustrate the effectiveness of our method on a gold-standard data set. The F1 score of our method is 77.34 and it remarkably outperforms the other baselines. Conclusions: We developed a distantly supervised NER approach to extract medical entity from TCM clinical records. We estimated our approach on a TCM clinical record data set. Our experimental results indicate that the proposed approach achieves a better performance than other baselines.

As the integral part of the new generation of information technology, the Internet of things significantly accelerates the intelligent sensing and data fusion in different industrial processes including mining, assisting people to make appropriate decision. These days, an increasing number of coal mine disasters pose a serious threat to people’s lives and property especially in several developing countries. In order to assess the risks arisen from gas explosion or gas poisoning, wireless sensor data should be processed and classified efficiently. Due to the fact that the “negative samples” of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. Using the popular deep neural networks, convolutional neural network and long short-term memory model, as comparisons, a number of experiments are carried out on several UCI machine learning datasets with different features. Experimental results show that least squares support vector machine optimized by swarm intelligence techniques can effectively handle classification task on different datasets especially on those datasets with limited samples and mixed attributes. The application of least squares support vector machine optimized by swarm intelligence techniques on real coal mine data demonstrates that this algorithm can process the data accurately and timely, therefore can warn of the accidents early in mining workplace.