Using Word Order in Political Text Classification with Long Short-term Memory Models

2019 ◽  
Vol 28 (3) ◽  
pp. 395-411
Author(s):  
Charles Chang ◽  
Michael Masterson
Keyword(s):  
Text Classification ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Complicated Model ◽  
Time Dependencies ◽  
Political Speeches ◽  
Classification Tasks ◽  
Long Short Term Memory ◽  
Militarized Interstate Dispute

Political scientists often wish to classify documents based on their content to measure variables, such as the ideology of political speeches or whether documents describe a Militarized Interstate Dispute. Simple classifiers often serve well in these tasks. However, if words occurring early in a document alter the meaning of words occurring later in the document, using a more complicated model that can incorporate these time-dependent relationships can increase classification accuracy. Long short-term memory (LSTM) models are a type of neural network model designed to work with data that contains time dependencies. We investigate the conditions under which these models are useful for political science text classification tasks with applications to Chinese social media posts as well as US newspaper articles. We also provide guidance for the use of LSTM models.

scholarly journals Chinese Text Classification Model Based on Deep Learning

2018 ◽  
Vol 10 (11) ◽  
pp. 113 ◽  
Author(s):  
Yue Li ◽  
Xutao Wang ◽  
Pengjian Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Chinese Text ◽  
Text Classification ◽  
Short Term Memory ◽  
Classification Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

scholarly journals Structured Sparsification of Gated Recurrent Neural Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 4989-4996
Author(s):  
Ekaterina Lobacheva ◽  
Nadezhda Chirkova ◽  
Alexander Markovich ◽  
Dmitry Vetrov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Language Modeling ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Network Compression

One of the most popular approaches for neural network compression is sparsification — learning sparse weight matrices. In structured sparsification, weights are set to zero by groups corresponding to structure units, e. g. neurons. We further develop the structured sparsification approach for the gated recurrent neural networks, e. g. Long Short-Term Memory (LSTM). Specifically, in addition to the sparsification of individual weights and neurons, we propose sparsifying the preactivations of gates. This makes some gates constant and simplifies an LSTM structure. We test our approach on the text classification and language modeling tasks. Our method improves the neuron-wise compression of the model in most of the tasks. We also observe that the resulting structure of gate sparsity depends on the task and connect the learned structures to the specifics of the particular tasks.

scholarly journals An Improved Double Channel Long Short-Term Memory Model for Medical Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shengbin Liang ◽  
Xinan Chen ◽  
Jixin Ma ◽  
Wencai Du ◽  
Huawei Ma
Keyword(s):  
Text Classification ◽  
Short Term Memory ◽  
Short Term ◽  
Model Learning ◽  
Current Time ◽  
Term Memory ◽  
Medical Text ◽  
Word Level ◽  
Long Short Term Memory ◽  
Double Channel

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.

scholarly journals Vietnamese Text Classification Algorithm using Long Short Term Memory and Word2Vec

2020 ◽  
Vol 19 (6) ◽  
pp. 1255-1279
Author(s):  
Huu Nguyen Phat ◽  
Nguyen Thi Minh Anh
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Industrial Revolution ◽  
Short Term Memory ◽  
Text Processing ◽  
Research Effort ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In the context of the ongoing forth industrial revolution and fast computer science development the amount of textual information becomes huge. So, prior to applying the seemingly appropriate methodologies and techniques to the above data processing their nature and characteristics should be thoroughly analyzed and understood. At that, automatic text processing incorporated in the existing systems may facilitate many procedures. So far, text classification is one of the basic applications to natural language processing accounting for such factors as emotions’ analysis, subject labeling etc. In particular, the existing advancements in deep learning networks demonstrate that the proposed methods may fit the documents’ classifying, since they possess certain extra efficiency; for instance, they appeared to be effective for classifying texts in English. The thorough study revealed that practically no research effort was put into an expertise of the documents in Vietnamese language. In the scope of our study, there is not much research for documents in Vietnamese. The development of deep learning models for document classification has demonstrated certain improvements for texts in Vietnamese. Therefore, the use of long short term memory network with Word2vec is proposed to classify text that improves both performance and accuracy. The here developed approach when compared with other traditional methods demonstrated somewhat better results at classifying texts in Vietnamese language. The evaluation made over datasets in Vietnamese shows an accuracy of over 90%; also the proposed approach looks quite promising for real applications.

scholarly journals Feature-Enhanced Nonequilibrium Bidirectional Long Short-Term Memory Model for Chinese Text Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 199629-199637
Author(s):  
Hai Huan ◽  
Jiayu Yan ◽  
Yaqin Xie ◽  
Yifei Chen ◽  
Pengcheng Li ◽  
...  
Keyword(s):  
Chinese Text ◽  
Text Classification ◽  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Chinese Text Classification ◽  
Long Short Term Memory

scholarly journals Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 106 ◽  
Author(s):  
Che-Wen Chen ◽  
Shih-Pang Tseng ◽  
Ta-Wen Kuan ◽  
Jhing-Fa Wang
Keyword(s):  
Text Classification ◽  
Short Term Memory ◽  
Family Doctor ◽  
Training Data ◽  
Service Robots ◽  
Service Robot ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge.

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