A convolutional attentional neural network for sentiment classification

Neural network models with attention mechanism have shown their efficiencies on various tasks. However, there is little research work on attention mechanism for text classification and existing attention model for text classification lacks of cognitive intuition and mathematical explanation. In this paper, we propose a new architecture of neural network based on the attention model for text classification. In particular, we show that the convolutional neural network (CNN) is a reasonable model for extracting attentions from text sequences in mathematics. We then propose a novel attention model base on CNN and introduce a new network architecture which combines recurrent neural network with our CNN-based attention model. Experimental results on five datasets show that our proposed models can accurately capture the salient parts of sentences to improve the performance of text classification

Research On Text Classification Based On Deep Neural Network

Text classification is one of the classic tasks in the field of natural language processing. The goal is to identify the category to which the text belongs. Text categorization is widely used in email detection, sentiment analysis, topic marking and other fields. However, good text representation is the key to improve the performance of natural language processing tasks such as text classification. Traditional text representation adopts bag-of-words model or vector space model, which not only loses the context information of the text, but also faces the problems of high latitude and high sparsity. In recent years, with the increase of data and the improvement of computing performance, the use of deep learning technology to represent and classify texts has attracted great attention. Convolutional neural network, recurrent neural network and recurrent neural network with attention mechanism are used to represent the text, and then to classify the text and other natural language processing tasks, all of which have better performance than the traditional methods. In this paper, we design two sentence-level text representation and classification models based on the deep network. The details are as follows: (1) Text representation and classification model based on bidirectional cyclic and convolutional neural networks-BRCNN. Brcnn's input is the word vector corresponding to each word in the sentence; After using cyclic neural network to extract word order information in sentences, convolution neural network is used to extract higher-level features of sentences. After convolution, the maximum pool operation is used to obtain sentence vectors. At last, softmax classifier is used for classification. Cyclic neural network can capture the word order information in sentences, while convolutional neural network can extract useful features. Experiments on eight text classification tasks show that BRCNN model can get better text feature representation, and the classification accuracy rate is equal to or higher than that of the prior art.. (2) A text representation and classification model based on attention mechanism and convolutional neural network-ACNN. ACNN model uses the recurrent neural network with attention mechanism to obtain the context vector; Then convolution neural network is used to extract more advanced feature information. The maximum pool operation is adopted to obtain a sentence vector; At last, the softmax classifier is used to classify the text. Experiments on eight text classification benchmark data sets show that ACNN improves the stability of model convergence, and can converge to an optimal or local optimal solution better than BRCNN

News Text Classification Based on an Improved Convolutional Neural Network

With the explosive growth in Internet news media and the disorganized status of news texts, this paper puts forward an automatic classification model for news based on a Convolutional Neural Network (CNN). In the model, Word2vec is firstly merged with Latent Dirichlet Allocation (LDA) to generate an effective text feature representation. Then when an attention mechanism is combined with the proposed model, higher attention probability values are given to key features to achieve an accurate judgment. The results show that the precision rate, the recall rate and the F1 value of the model in this paper reach 96.4%, 95.9% and 96.2% respectively, which indicates that the improved CNN, through a unique framework, can extract deep semantic features of the text and provide a strong support for establishing an efficient and accurate news text classification model

Learning to detect chest radiographs containing lung nodules using visual attention networks

Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiological reports associated to the radiographs. Using a repositotory of over 700,000 chest radiographs, in this study we demonstrate that promising nodule detection performance can be achieved using weak labels through convolutional neural networks for radiograph classification. We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available. Annotated nodules are used at training time to deliver a visual attention mechanism informing the model about its localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the estimated position of a nodule against the ground truth, when this is available. A corresponding localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning. When a nodule annotation is available at training time, the reward function is modified accordingly so that exploring portions of the radiographs away from a nodule incurs a larger penalty. Our empirical results demonstrate the potential advantages of these architectures in comparison to competing methodologies

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network