Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from l...Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.展开更多
Newspaper is, to some extent, a mirror of our society, reflecting the latest change and development of the society.News text is a linguistic representation of the world. This paper is to briefly introduce the structur...Newspaper is, to some extent, a mirror of our society, reflecting the latest change and development of the society.News text is a linguistic representation of the world. This paper is to briefly introduce the structure, writing and linguistic styles of news texts and thus to increase readers' awareness of the distinctive features of news texts.展开更多
In this paper,the problem of increasing information transfer authenticity is formulated.And to reach a decision,the control methods and algorithms based on the use of statistical and structural information redundancy ...In this paper,the problem of increasing information transfer authenticity is formulated.And to reach a decision,the control methods and algorithms based on the use of statistical and structural information redundancy are presented.It is assumed that the controllable information is submitted as the text element images and it contains redundancy,caused by statistical relations and non-uniformity probability distribution of the transmitted data.The use of statistical redundancy allows to develop the adaptive rules of the authenticity control which take into account non-stationarity properties of image data while transferring the information.The structural redundancy peculiar to the container of image in a data transfer package is used for developing new rules to control the information authenticity on the basis of pattern recognition mechanisms.The techniques offered in this work are used to estimate the authenticity in structure of data transfer packages.The results of comparative analysis for developed methods and algorithms show that their parameters of efficiency are increased by criterion of probability of undetected mistakes,labour input and cost of realization.展开更多
With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text...With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.展开更多
Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,m...Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.展开更多
Introduction:Traditional dietary surveys are timeconsuming,and manual recording may lead to omissions.Improvement during data collection is essential to enhance accuracy of nutritional surveys.In recent years,large la...Introduction:Traditional dietary surveys are timeconsuming,and manual recording may lead to omissions.Improvement during data collection is essential to enhance accuracy of nutritional surveys.In recent years,large language models(LLMs)have been rapidly developed,which can provide text-processing functions and assist investigators in conducting dietary surveys.Methods:Thirty-eight participants from 15 families in the Huangpu and Jiading districts of Shanghai were selected.A standardized 24-hour dietary recall protocol was conducted using an intelligent recording pen that simultaneously captured audio data.These recordings were then transcribed into text.After preprocessing,we used GLM-4 for prompt engineering and chain-of-thought for collaborative reasoning,output structured data,and analyzed its integrity and consistency.Model performance was evaluated using precision and F1 scores.Results:The overall integrity rate of the LLMbased structured data reached 92.5%,and the overall consistency rate compared with manual recording was 86%.The LLM can accurately and completely recognize the names of ingredients and dining and production locations during the transcription.The LLM achieved 94%precision and an F1 score of 89.7%for the full dataset.Conclusion:LLM-based text recognition and structured data extraction can serve as effective auxiliary tools to improve efficiency and accuracy in traditional dietary surveys.With the rapid advancement of artificial intelligence,more accurate and efficient auxiliary tools can be developed for more precise and efficient data collection in nutrition research.展开更多
The present study probed into the effects of text structure, structure awareness and proficiency level on EFL learners＇ reading test performance. There are 112 college-level students participated in the experiment an...The present study probed into the effects of text structure, structure awareness and proficiency level on EFL learners＇ reading test performance. There are 112 college-level students participated in the experiment and their English proficiency belonged to distinct levels. The subjects＇ performance on the recall of two passages written in different types of structure was examined. Results of statistical indicate that text structure, structure awareness and proficiency level all have main effects on the subjects＇ reading performance. More specifically, two major findings emerged from the results of the investigation. One the one hand, text structures significantly affected the quantity but not the quality of the information recalled while proficiency level and structure awareness had significant impact on both the quantity and quality of information recalled. On the other hand, structure awareness was irrelevant to either text structure or proficiency level. The implications of the findings for teaching L2/FL reading were suggested.展开更多
In 1961 T. B. Jones and J. W. Snyder published 332 texts from many collections in the United States in their book Sumerian Economic Texts from the Third Ur Dynasty, a Catalogue and Discussion of Documents from Various...In 1961 T. B. Jones and J. W. Snyder published 332 texts from many collections in the United States in their book Sumerian Economic Texts from the Third Ur Dynasty, a Catalogue and Discussion of Documents from Various Collections (Minneapolis)展开更多
Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translat...Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translated texts on Neijing. Due to the diversity of editions and different understanding, the translating styles and contents are widely different. This study will focus on the different translating styles on culture-specific lexicon、figure of speech and four-Chinese-character structures in Neijin.展开更多
Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper propo...Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper proposes a new GSPT-CVAE model(Graph Structured Processing,Single Vector,and Potential Attention Com-puting Transformer-Based Conditioned Variational Autoencoder model).The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text,and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation.In the process of potential representation guiding text generation,the model adopts a combination of traditional embedding and potential attention calculation to give full play to the guiding role of potential representation for generating text,to improve the controllability and effectiveness of text generation.The experimental results show that the model has excellent representation learning ability and can learn rich and useful textual relationship representations.The model also achieves satisfactory results in the effectiveness and controllability of text generation and can generate long texts that match the given constraints.The ROUGE-1 F1 score of this model is 0.243,the ROUGE-2 F1 score is 0.041,the ROUGE-L F1 score is 0.22,and the PPL-Word score is 34.303,which gives the GSPT-CVAE model a certain advantage over the baseline model.Meanwhile,this paper compares this model with the state-of-the-art generative models T5,GPT-4,Llama2,and so on,and the experimental results show that the GSPT-CVAE model has a certain competitiveness.展开更多
Article Categories⋅Research article is a complete academic investigation that covers a significant advance in a specialty.It usually includes a structured abstract under 300 words,an introduction,sections with heading...Article Categories⋅Research article is a complete academic investigation that covers a significant advance in a specialty.It usually includes a structured abstract under 300 words,an introduction,sections with headings of Materials and Methods,Results,and Discussion,and References.Meta-analyses are published as original articles as well.The full text is about 3500 words and the figures and tables need to be kept under 7 items.展开更多
The theory of"Life and Practice"education was developed based on Tao Xingzhi's theory of life education from the early 20th century.Tao Xingzhi's theory of life education continues to profoundly infl...The theory of"Life and Practice"education was developed based on Tao Xingzhi's theory of life education from the early 20th century.Tao Xingzhi's theory of life education continues to profoundly influence contemporary and future educational reforms.The 21st century is an era of information and artificial intelligence,with the third major transformation undergoing in the field of education.Compared to the era in which Tao Xingzhi lived,China has experienced tremendous changes in its social structure and educational system.Therefore,to adapt to the requirements of this new era,the theory of"Life and Practiceeducation has emerged by inheriting and developing Tao Xingzhi's theory of life education.The core goal of"Life and Practice"education is to address the disconnect between education and life,school and society,and teaching and practice.展开更多
基金the Special Project of the Shanghai Municipal Commission of Economy and Information Technology for Promoting High-Quality Industrial Development(No.2024-GZL-RGZN-02011)the Shanghai City Digital Transformation Project(No.202301002)the Project of Shanghai Shenkang Hospital Development Center(No.SHDC22023214)。
文摘Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.
文摘Newspaper is, to some extent, a mirror of our society, reflecting the latest change and development of the society.News text is a linguistic representation of the world. This paper is to briefly introduce the structure, writing and linguistic styles of news texts and thus to increase readers' awareness of the distinctive features of news texts.
文摘In this paper,the problem of increasing information transfer authenticity is formulated.And to reach a decision,the control methods and algorithms based on the use of statistical and structural information redundancy are presented.It is assumed that the controllable information is submitted as the text element images and it contains redundancy,caused by statistical relations and non-uniformity probability distribution of the transmitted data.The use of statistical redundancy allows to develop the adaptive rules of the authenticity control which take into account non-stationarity properties of image data while transferring the information.The structural redundancy peculiar to the container of image in a data transfer package is used for developing new rules to control the information authenticity on the basis of pattern recognition mechanisms.The techniques offered in this work are used to estimate the authenticity in structure of data transfer packages.The results of comparative analysis for developed methods and algorithms show that their parameters of efficiency are increased by criterion of probability of undetected mistakes,labour input and cost of realization.
文摘With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.
基金supported by Yunnan Provincial Education Department Science Foundation of China under Grant construction of the seventh batch of key engineering research centers in colleges and universities(Grant Project:Yunnan College and University Edge Computing Network Engineering Research Center).
文摘Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.
基金Supported by the Ministry of Finance of the People’s Republic of China from 2022 to 2024(grant number 102393220020070000016).
文摘Introduction:Traditional dietary surveys are timeconsuming,and manual recording may lead to omissions.Improvement during data collection is essential to enhance accuracy of nutritional surveys.In recent years,large language models(LLMs)have been rapidly developed,which can provide text-processing functions and assist investigators in conducting dietary surveys.Methods:Thirty-eight participants from 15 families in the Huangpu and Jiading districts of Shanghai were selected.A standardized 24-hour dietary recall protocol was conducted using an intelligent recording pen that simultaneously captured audio data.These recordings were then transcribed into text.After preprocessing,we used GLM-4 for prompt engineering and chain-of-thought for collaborative reasoning,output structured data,and analyzed its integrity and consistency.Model performance was evaluated using precision and F1 scores.Results:The overall integrity rate of the LLMbased structured data reached 92.5%,and the overall consistency rate compared with manual recording was 86%.The LLM can accurately and completely recognize the names of ingredients and dining and production locations during the transcription.The LLM achieved 94%precision and an F1 score of 89.7%for the full dataset.Conclusion:LLM-based text recognition and structured data extraction can serve as effective auxiliary tools to improve efficiency and accuracy in traditional dietary surveys.With the rapid advancement of artificial intelligence,more accurate and efficient auxiliary tools can be developed for more precise and efficient data collection in nutrition research.
文摘The present study probed into the effects of text structure, structure awareness and proficiency level on EFL learners＇ reading test performance. There are 112 college-level students participated in the experiment and their English proficiency belonged to distinct levels. The subjects＇ performance on the recall of two passages written in different types of structure was examined. Results of statistical indicate that text structure, structure awareness and proficiency level all have main effects on the subjects＇ reading performance. More specifically, two major findings emerged from the results of the investigation. One the one hand, text structures significantly affected the quantity but not the quality of the information recalled while proficiency level and structure awareness had significant impact on both the quantity and quality of information recalled. On the other hand, structure awareness was irrelevant to either text structure or proficiency level. The implications of the findings for teaching L2/FL reading were suggested.
文摘In 1961 T. B. Jones and J. W. Snyder published 332 texts from many collections in the United States in their book Sumerian Economic Texts from the Third Ur Dynasty, a Catalogue and Discussion of Documents from Various Collections (Minneapolis)
文摘Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translated texts on Neijing. Due to the diversity of editions and different understanding, the translating styles and contents are widely different. This study will focus on the different translating styles on culture-specific lexicon、figure of speech and four-Chinese-character structures in Neijin.
文摘Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper proposes a new GSPT-CVAE model(Graph Structured Processing,Single Vector,and Potential Attention Com-puting Transformer-Based Conditioned Variational Autoencoder model).The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text,and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation.In the process of potential representation guiding text generation,the model adopts a combination of traditional embedding and potential attention calculation to give full play to the guiding role of potential representation for generating text,to improve the controllability and effectiveness of text generation.The experimental results show that the model has excellent representation learning ability and can learn rich and useful textual relationship representations.The model also achieves satisfactory results in the effectiveness and controllability of text generation and can generate long texts that match the given constraints.The ROUGE-1 F1 score of this model is 0.243,the ROUGE-2 F1 score is 0.041,the ROUGE-L F1 score is 0.22,and the PPL-Word score is 34.303,which gives the GSPT-CVAE model a certain advantage over the baseline model.Meanwhile,this paper compares this model with the state-of-the-art generative models T5,GPT-4,Llama2,and so on,and the experimental results show that the GSPT-CVAE model has a certain competitiveness.
文摘Article Categories⋅Research article is a complete academic investigation that covers a significant advance in a specialty.It usually includes a structured abstract under 300 words,an introduction,sections with headings of Materials and Methods,Results,and Discussion,and References.Meta-analyses are published as original articles as well.The full text is about 3500 words and the figures and tables need to be kept under 7 items.
文摘The theory of"Life and Practice"education was developed based on Tao Xingzhi's theory of life education from the early 20th century.Tao Xingzhi's theory of life education continues to profoundly influence contemporary and future educational reforms.The 21st century is an era of information and artificial intelligence,with the third major transformation undergoing in the field of education.Compared to the era in which Tao Xingzhi lived,China has experienced tremendous changes in its social structure and educational system.Therefore,to adapt to the requirements of this new era,the theory of"Life and Practiceeducation has emerged by inheriting and developing Tao Xingzhi's theory of life education.The core goal of"Life and Practice"education is to address the disconnect between education and life,school and society,and teaching and practice.
