Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification

Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.

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“…is redundant detail may contain some sort of knowledge too. us, the classification of long text requires an efficient model [6].…”

Section: Introductionmentioning

confidence: 99%

Evolving Long Short-Term Memory Network-Based Text Classification

Singh

Dargar

Gupta³

et al. 2022

Computational Intelligence and Neuroscience

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“…The probability of the switch can be worked out based on the encoder/decoder context vector and the current hiddenlayer state, as in [29]: denotes the bias term. The weight matrix and bias term in (7) are parameters that can be updated during the model training iteration. The linear weighted sum of these parameters is nonlinearly activated by the sigmoid function and mapped between 0 and 1 as a soft switch to control the source of the input layer, based on the information of the two partsthe source text and the vocabulary.…”

Section: B 'Da-pn' Modelmentioning

confidence: 99%

“…With a RNN, each word is entered into the model according to its order of appearance in the original text, which is good for recording the sequence information of the text but limits the speed of network training and text summary generation. If both the encoder and decoder adopt a Convolutional Neural Network (CNN) [7,8], this will allow the model to use parallel computing in the phase of training, which will not only maintain accuracy but will also improve efficiency. At the same time, if multi-step attention is used [9], every layer of the semantic vector, generated in the decoder, would look forward, which could further improve accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization

et al. 2021

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The automatic generation of a text summary is a task of generating a short summary for a relatively long text document by capturing its key information. In the past, supervised statistical machine learning was widely used for this Automatic Text Summarization (ATS) task, but due to its high dependence on the quality of text features, the generated summaries lack accuracy and coherence, while the computational power involved, and performance achieved, could not easily meet the current needs. This paper proposes four novel ATS models with a Sequence-to-Sequence (Seq2Seq) structure, utilizing an attention-based bidirectional Long Short-Term Memory (LSTM), with added enhancements for increasing the correlation between the generated text summary and the source text, and solving the problem of unregistered words, suppressing the repeated words, and preventing the spread of cumulative errors in generated text summaries. Experiments conducted on two public data sets confirmed that the proposed ATS models achieve indeed better performance than the baselines and some of the state-of-the-art models considered.

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“…As a well-known deep learning method, CNN (Convolutional Neural Network) has been widely utilized in various pattern recognition areas such as medical image analysis [33], crop mapping [34], text classification [35], fraud identification [36], voice recognition [37], and so on. CNN has already been used for the sensor faults diagnosis of UAV [38], rotating machinery [19] and the multi-operation forging process [39].…”

Section: Diagnosis Model Based On Modified Cnnmentioning

confidence: 99%

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

et al. 2021

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In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.

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Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification

Cited by 104 publications

References 7 publications

Evolving Long Short-Term Memory Network-Based Text Classification

Evolving Long Short-Term Memory Network-Based Text Classification

Enhancements of Attention-Based Bidirectional LSTM for Hybrid Automatic Text Summarization

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

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