Neural networks and conditional random fields based approach for effective question processing

Bhaskaran, Sruthy K; Sreejith, C.; Rafeeque, P C

doi:10.1016/j.procs.2018.10.381

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In another investigation conducted by Bhaskaran et al [19], CRF and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms were compared in which, in the context of domain classification, the acuity values of 92.17% and 90.96% were achieved, respectively. These values were obtained using the GloVe model concerning the data processing component.…”

Section: Data Classification Results -Mlmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

Machado

Sá

2021

Advances in Intelligent Systems and Computing

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The domain classification of scientific knowledge objects has been continuously improved over the years. Systems that can automatically classify a scientific knowledge object, through the use of artificial intelligence, machine learning algorithms, natural language processing, and others, have been adopted in most scientific knowledge databases to maintain internal classification consistency as well as to simplify the information arrangement. However, the amount of available data has grown exponentially in the last few years and now it can be found in multiple platforms under different classifications due to the implementation of different classification systems. Thus, the process of searching and selecting relevant data in research studies and projects has become more complex and the time needed to find the right information has continuously grown as well. Therefore, machine learning and natural language processing play an important role in the development and achievement of automatic and standardized classification systems that will aid researchers in their research work.

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Section: Data Classification Results -Mlmentioning

confidence: 99%

Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

Machado

Sá

2021

Advances in Intelligent Systems and Computing

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“…Especially in the field of broken information recovery, CNNs are able to recognize and reconstruct lost or damaged parts of information by learning a large amount of data. To address the limitations of traditional deep convolutional neural networks in processing complex or highly broken information, researchers have proposed a variety of improved algorithms [4][5][6][7]. These algorithms have improved the accuracy and efficiency of the recovery results by introducing new network structures, optimizing the training process, and combining other deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Zhou

2024

Applied Mathematics and Nonlinear Sciences

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This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.

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Arabic Question Answering Systems: Gap Analysis

2021

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Question-answering (QA) systems aim to provide answers for given questions. The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research.

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Neural networks and conditional random fields based approach for effective question processing

Cited by 3 publications

References 12 publications

Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

Machine Learning and Natural Language Processing in Domain Classification of Scientific Knowledge Objects: A Review

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Arabic Question Answering Systems: Gap Analysis

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