In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in sema...In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.展开更多
Text classification means to assign a document to one or more classes or categories according to content. Text classification provides convenience for users to obtain data. Because of the polysemy of text data, multi-...Text classification means to assign a document to one or more classes or categories according to content. Text classification provides convenience for users to obtain data. Because of the polysemy of text data, multi-label classification can handle text data more comprehensively. Multi-label text classification become the key problem in the data mining. To improve the performances of multi-label text classification, semantic analysis is embedded into the classification model to complete label correlation analysis, and the structure, objective function and optimization strategy of this model is designed. Then, the convolution neural network(CNN) model based on semantic embedding is introduced. In the end, Zhihu dataset is used for evaluation. The result shows that this model outperforms the related work in terms of recall and area under curve(AUC) metrics.展开更多
Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages suc...Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.展开更多
Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a...Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.展开更多
Assigning standardized International Classification of Disease(ICD)codes to Electronic Medical Records(EMR)is crucial for enhancing the efficiency and accuracy of medical coding processes.However,existing methods face...Assigning standardized International Classification of Disease(ICD)codes to Electronic Medical Records(EMR)is crucial for enhancing the efficiency and accuracy of medical coding processes.However,existing methods face challenges in effectively capturing,integrating,and amalgamating specialized medical knowledge from complex textual data.In this study,we propose GoM-ICD,an advanced automatic ICD coding framework that integrates multiple gap schemes with a Mixture of Experts(MoE)architecture.GoM-ICD is designed to address the extreme multilabel text classification in ICD coding.It segments and reassembles text to facilitate seamless information exchange across different chunks,employing various segmentation methods derived from different gap schemes.Then the model-level MoE consolidates the predictions of these methods to enhance the prediction performance.Specifically,the segmented text is input to a Pretrained Language Model(PLM)to extract textual features.Next,a Bidirectional Long Short-Term Memory network(BiLSTM)is employed to capture long-term contextual information from the textual features.Finally,a text-level MoE,followed by a label-level MoE,enables precise attention matching between text and labels,thereby improving the fidelity of the coding process.The three levels of MoE leverage the collective insights of diverse expert models,effectively processing multi-dimensional text features and unifying model-level insights from various gap schemes.Extensive experimental results demonstrate that GoM-ICD achieves the state-of-the-art performance in automatic ICD coding tasks,reaching micro-F1 of 0.617,0.620,and 0.613 on datasets MIMIC III full,MIMIC-III clean,and MIMIC-IV ICD-10,respectively.The source code can be obtained from https://github.com/CSUBioGroup/GoM-ICD.展开更多
基金supported by National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2020040,ZDYF2021GXJS003)+2 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant Nos.620MS021,621QN211)Science and Technology Development Center of the Ministry of Education Industry-University-Research Innovation Fund(2021JQR017).
文摘In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.
文摘Text classification means to assign a document to one or more classes or categories according to content. Text classification provides convenience for users to obtain data. Because of the polysemy of text data, multi-label classification can handle text data more comprehensively. Multi-label text classification become the key problem in the data mining. To improve the performances of multi-label text classification, semantic analysis is embedded into the classification model to complete label correlation analysis, and the structure, objective function and optimization strategy of this model is designed. Then, the convolution neural network(CNN) model based on semantic embedding is introduced. In the end, Zhihu dataset is used for evaluation. The result shows that this model outperforms the related work in terms of recall and area under curve(AUC) metrics.
基金supported by the NSFC (Grant Nos. 61772281,61703212, 61602254)Jiangsu Province Natural Science Foundation [grant numberBK2160968]the Priority Academic Program Development of Jiangsu Higher Edu-cationInstitutions (PAPD) and Jiangsu Collaborative Innovation Center on AtmosphericEnvironment and Equipment Technology (CICAEET).
文摘Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.
基金supported by National Natural Science Foundation of China[No.41971349,No.41930107,No.42090010 and No.41501434]National Key Research and Development Program of China[No.2017YFB0503704 and No.2018YFC0809806].
文摘Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.
文摘Assigning standardized International Classification of Disease(ICD)codes to Electronic Medical Records(EMR)is crucial for enhancing the efficiency and accuracy of medical coding processes.However,existing methods face challenges in effectively capturing,integrating,and amalgamating specialized medical knowledge from complex textual data.In this study,we propose GoM-ICD,an advanced automatic ICD coding framework that integrates multiple gap schemes with a Mixture of Experts(MoE)architecture.GoM-ICD is designed to address the extreme multilabel text classification in ICD coding.It segments and reassembles text to facilitate seamless information exchange across different chunks,employing various segmentation methods derived from different gap schemes.Then the model-level MoE consolidates the predictions of these methods to enhance the prediction performance.Specifically,the segmented text is input to a Pretrained Language Model(PLM)to extract textual features.Next,a Bidirectional Long Short-Term Memory network(BiLSTM)is employed to capture long-term contextual information from the textual features.Finally,a text-level MoE,followed by a label-level MoE,enables precise attention matching between text and labels,thereby improving the fidelity of the coding process.The three levels of MoE leverage the collective insights of diverse expert models,effectively processing multi-dimensional text features and unifying model-level insights from various gap schemes.Extensive experimental results demonstrate that GoM-ICD achieves the state-of-the-art performance in automatic ICD coding tasks,reaching micro-F1 of 0.617,0.620,and 0.613 on datasets MIMIC III full,MIMIC-III clean,and MIMIC-IV ICD-10,respectively.The source code can be obtained from https://github.com/CSUBioGroup/GoM-ICD.
