scholarly journals A Neural Network for Text Representation

2007 ◽  
pp. 667-672 ◽  
Author(s):  
Mikaela Keller ◽  
Samy Bengio
Keyword(s):  
Neural Network ◽  
Text Representation

scholarly journals Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hu Wang ◽  
Tianbao Liang ◽  
Yanxia Cheng
Keyword(s):  
Neural Network ◽  
Subjective Evaluation ◽  
Perceived Value ◽  
Online Reviews ◽  
Classification Model ◽  
Text Representation ◽  
Labor Costs ◽  
Data Set ◽  
Short Text ◽  
Perceived Utility

Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.

scholarly journals Incorporating knowledge into neural network for text representation

2018 ◽  
Vol 96 ◽  
pp. 103-114 ◽  
Author(s):  
Yiming Li ◽  
Baogang Wei ◽  
Yonghuai Liu ◽  
Liang Yao ◽  
Hui Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Text Representation

scholarly journals Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ayoub Bagheri ◽  
T. Katrien J. Groenhof ◽  
Folkert W. Asselbergs ◽  
Saskia Haitjema ◽  
Michiel L. Bots ◽  
...  
Keyword(s):  
Neural Network ◽  
Text Mining ◽  
Cardiovascular Events ◽  
Misclassification Rate ◽  
Text Representation ◽  
Clinical Predictors ◽  
X Ray ◽  
Major Cardiovascular Events ◽  
Radiology Reports ◽  
Chest X Ray

Background and Objective. Electronic health records (EHRs) contain free-text information on symptoms, diagnosis, treatment, and prognosis of diseases. However, this potential goldmine of health information cannot be easily accessed and used unless proper text mining techniques are applied. The aim of this project was to develop and evaluate a text mining pipeline in a multimodal learning architecture to demonstrate the value of medical text classification in chest radiograph reports for cardiovascular risk prediction. We sought to assess the integration of various text representation approaches and clinical structured data with state-of-the-art deep learning methods in the process of medical text mining. Methods. We used EHR data of patients included in the Second Manifestations of ARTerial disease (SMART) study. We propose a deep learning-based multimodal architecture for our text mining pipeline that integrates neural text representation with preprocessed clinical predictors for the prediction of recurrence of major cardiovascular events in cardiovascular patients. Text preprocessing, including cleaning and stemming, was first applied to filter out the unwanted texts from X-ray radiology reports. Thereafter, text representation methods were used to numerically represent unstructured radiology reports with vectors. Subsequently, these text representation methods were added to prediction models to assess their clinical relevance. In this step, we applied logistic regression, support vector machine (SVM), multilayer perceptron neural network, convolutional neural network, long short-term memory (LSTM), and bidirectional LSTM deep neural network (BiLSTM). Results. We performed various experiments to evaluate the added value of the text in the prediction of major cardiovascular events. The two main scenarios were the integration of radiology reports (1) with classical clinical predictors and (2) with only age and sex in the case of unavailable clinical predictors. In total, data of 5603 patients were used with 5-fold cross-validation to train the models. In the first scenario, the multimodal BiLSTM (MI-BiLSTM) model achieved an area under the curve (AUC) of 84.7%, misclassification rate of 14.3%, and F1 score of 83.8%. In this scenario, the SVM model, trained on clinical variables and bag-of-words representation, achieved the lowest misclassification rate of 12.2%. In the case of unavailable clinical predictors, the MI-BiLSTM model trained on radiology reports and demographic (age and sex) variables reached an AUC, F1 score, and misclassification rate of 74.5%, 70.8%, and 20.4%, respectively. Conclusions. Using the case study of routine care chest X-ray radiology reports, we demonstrated the clinical relevance of integrating text features and classical predictors in our text mining pipeline for cardiovascular risk prediction. The MI-BiLSTM model with word embedding representation appeared to have a desirable performance when trained on text data integrated with the clinical variables from the SMART study. Our results mined from chest X-ray reports showed that models using text data in addition to laboratory values outperform those using only known clinical predictors.

scholarly journals Long Text QA Matching Model Based on BiGRU–DAttention–DSSM

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1129
Author(s):  
Shihong Chen ◽  
Tianjiao Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Matching Task ◽  
Semantic Model ◽  
Text Representation ◽  
Short Text ◽  
Questions And Answers ◽  
Text Features ◽  
Text Matching

QA matching is a very important task in natural language processing, but current research on text matching focuses more on short text matching rather than long text matching. Compared with short text matching, long text matching is rich in information, but distracting information is frequent. This paper extracted question-and-answer pairs about psychological counseling to research long text QA-matching technology based on deep learning. We adjusted DSSM (Deep Structured Semantic Model) to make it suitable for the QA-matching task. Moreover, for better extraction of long text features, we also improved DSSM by enriching the text representation layer, using a bidirectional neural network and attention mechanism. The experimental results show that BiGRU–Dattention–DSSM performs better at matching questions and answers.

Hybrid Deep Neural Network-Based Text Representation Model to Improve Microblog Retrieval

2020 ◽  
Vol 51 (2) ◽  
pp. 115-139
Author(s):  
Ibtihel Ben Ltaifa ◽  
Lobna Hlaoua ◽  
Lotfi Ben Romdhane
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Text Representation ◽  
Representation Model

SE4ExSum: An Integrated Semantic-aware Neural Approach with Graph Convolutional Network for Extractive Text Summarization

2021 ◽  
Vol 20 (6) ◽  
pp. 1-22
Author(s):  
Tham Vo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
State Of The Art ◽  
Text Summarization ◽  
Text Representation ◽  
Convolutional Network ◽  
Sentence Level ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Common Problems

Recently, advanced techniques in deep learning such as recurrent neural network (GRU, LSTM and Bi-LSTM) and auto-encoding (attention-based transformer and BERT) have achieved great successes in multiple application domains including text summarization. Recent state-of-the-art encoding-based text summarization models such as BertSum, PreSum and DiscoBert have demonstrated significant improvements on extractive text summarization tasks. However, recent models still encounter common problems related to the language-specific dependency which requires the supports of the external NLP tools. Besides that, recent advanced text representation methods, such as BERT as the sentence-level textual encoder, also fail to fully capture the representation of a full-length document. To address these challenges, in this paper we proposed a novel s emantic-ware e mbedding approach for ex tractive text sum marization , called as: SE4ExSum. Our proposed SE4ExSum is an integration between the use of feature graph-of-words (FGOW) with BERT-based encoder for effectively learning the word/sentence-level representations of a given document. Then, the g raph c onvolutional n etwork (GCN) based encoder is applied to learn the global document's representation which is then used to facilitate the text summarization task. Extensive experiments on benchmark datasets show the effectiveness of our proposed model in comparing with recent state-of-the-art text summarization models.

scholarly journals Language to language Translation using GRU method

2020 ◽  
Vol 5 (3) ◽  
pp. 192-194
Keyword(s):  
Neural Network ◽  
Language Translation ◽  
Text Representation ◽  
Concept System ◽  
Current State ◽  
Information Set ◽  
Audio Data ◽  
Interpretation Process ◽  
Translation Systems ◽  
The Voice

Current state of the art translation systems for speech to speech rely heavily on a text representation for the interpretation. By transcoding speech to text we lose important information about the characteristics of the voice like the emotion, pitch and accent. The thesis examine the likelihood of using an GRU neural network model to translate speech to speech without the requirement of a text representation that's by translating using the raw audio data directly so as to persevere the characteristics of the voice that otherwise stray within the text transcoding a part of the interpretation process. As a part of the research we create an information set of phrases suitable for speech to speech translation tasks. The thesis leads to a signal of concept system which requires scaling the underlying deep neural network so as to figure better.

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