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SF-CNN: Deep Text Classification and Retrieval for Text Documents

Intelligent Automation & Soft Computing 2023年第2期35卷 1799-1813页

作者： R.Sarasu K.K.Thyagharajan N.R.Shanker Computer Science and Engineering Dhanalaksmi College of EngineeringAnna UniversityChennaiIndia R.M.D Engineering College Anna UniversityChennaiIndia Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringAnna UniversityChennaiIndia

Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** efficient classification algorithm for retrieving documents based on keyword words is *** traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in *** solve the above problem,semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text *** proposed SF-CNN is based on deep semantic-based bag-of-word representation for document *** deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for *** experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN ***-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

关键词： semantic classiﬁcation convolution neural networks semantic enhancement

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SHEL:a semantically enhanced hardware-friendly entity linking method

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High Technology Letters 2024年第1期30卷 13-22页

作者：亓东林 CHEN Shudong DU Rong TONG Da YU Yong Institute of Microelectronics of Chinese Academy of Sciences Beijing 100029P.R.China University of Chinese Academy of Sciences Beijing 100190P.R.China

With the help of pre-trained language models,the accuracy of the entity linking task has made great strides in recent ***,most models with excellent performance require fine-tuning on a large amount of training data using large pre-trained language models,which is a hardware threshold to accomplish this *** researchers have achieved competitive results with less training data through ingenious methods,such as utilizing information provided by the named entity recognition *** paper presents a novel semantic-enhancement-based entity linking approach,named semantically enhanced hardware-friendly entity linking(SHEL),which is designed to be hardware friendly and efficient while maintaining good ***,SHEL's semantic enhancement approach consists of three aspects:(1)semantic compression of entity descriptions using a text summarization model;(2)maximizing the capture of mention contexts using asymmetric heuristics;(3)calculating a fixed size mention representation through pooling *** series of semantic enhancement methods effectively improve the model's ability to capture semantic information while taking into account the hardware constraints,and significantly improve the model's convergence speed by more than 50%compared with the strong baseline model proposed in this *** terms of performance,SHEL is comparable to the previous method,with superior performance on six well-established datasets,even though SHEL is trained using a smaller pre-trained language model as the encoder.

关键词： entity linking(EL) pre-trained models knowledge graph text summarization semantic enhancement

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