1
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Huang J, Yang DM, Rong R, Nezafati K, Treager C, Chi Z, Wang S, Cheng X, Guo Y, Klesse LJ, Xiao G, Peterson ED, Zhan X, Xie Y. A critical assessment of using ChatGPT for extracting structured data from clinical notes. NPJ Digit Med 2024; 7:106. [PMID: 38693429 PMCID: PMC11063058 DOI: 10.1038/s41746-024-01079-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/14/2024] [Indexed: 05/03/2024] Open
Abstract
Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.
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Affiliation(s)
- Jingwei Huang
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Kuroush Nezafati
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Colin Treager
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Zhikai Chi
- Department of Pathology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Xian Cheng
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Yujia Guo
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Laura J Klesse
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Eric D Peterson
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA.
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA.
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2
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Lai PT, Wei CH, Luo L, Chen Q, Lu Z. BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets. J Biomed Inform 2023; 146:104487. [PMID: 37673376 DOI: 10.1016/j.jbi.2023.104487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/18/2023] [Accepted: 09/02/2023] [Indexed: 09/08/2023]
Abstract
Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx's robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.
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Affiliation(s)
- Po-Ting Lai
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894 Bethesda, USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894 Bethesda, USA
| | - Ling Luo
- School of Computer Science and Technology, Dalian University of Technology, 116024 Dalian, China
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894 Bethesda, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894 Bethesda, USA.
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3
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Lai PT, Wei CH, Luo L, Chen Q, Lu Z. BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets. ARXIV 2023:arXiv:2306.11189v1. [PMID: 37502629 PMCID: PMC10370213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx's robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.
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Affiliation(s)
- Po-Ting Lai
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894, Bethesda, USA
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894, Bethesda, USA
| | - Ling Luo
- School of Computer Science and Technology, Dalian University of Technology, 116024, Dalian, China
| | - Qingyu Chen
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894, Bethesda, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894, Bethesda, USA
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4
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Yang Y, Lu Y, Yan W. A comprehensive review on knowledge graphs for complex diseases. Brief Bioinform 2023; 24:6931722. [PMID: 36528805 DOI: 10.1093/bib/bbac543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/02/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
In recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for storing relationships between entities and for performing higher level reasoning. KGs in biomedicine and clinical practice aim to provide an elegant solution for diagnosing and treating complex diseases more efficiently and flexibly. Here, we provide a systematic review to characterize the state-of-the-art of KGs in the area of complex disease research. We cover the following topics: (1) knowledge sources, (2) entity extraction methods, (3) relation extraction methods and (4) the application of KGs in complex diseases. As a result, we offer a complete picture of the domain. Finally, we discuss the challenges in the field by identifying gaps and opportunities for further research and propose potential research directions of KGs for complex disease diagnosis and treatment.
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Affiliation(s)
- Yang Yang
- School of Computer Science & Technology, Soochow University, Suzhou 215000, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Yuwei Lu
- School of Computer Science & Technology, Soochow University, Suzhou 215000, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Wenying Yan
- Department of Bioinformatics, School of Biology and Basic Medical Sciences, Medical College of Soochow University, and Center for Systems Biology, Soochow University, Suzhou 215123, China
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Li Z, Wang M, Peng D, Liu J, Xie Y, Dai Z, Zou X. Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information. Interdiscip Sci 2022; 14:683-696. [PMID: 35391615 DOI: 10.1007/s12539-022-00511-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
The identification of chemical-disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical-disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical-disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical-disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments.
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Affiliation(s)
- Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China.
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangzhou, 510006, People's Republic of China.
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of State Administration of Traditional Chinese Medicine, Guangzhou, 510006, People's Republic of China.
| | - Mengru Wang
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China
| | - Dongdong Peng
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China
| | - Jie Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China
| | - Yun Xie
- HuiZhou University, Huizhou, 516007, People's Republic of China
| | - Zong Dai
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Xiaoyong Zou
- School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
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6
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Hao T, Huang Z, Liang L, Weng H, Tang B. Health Natural Language Processing: Methodology Development and Applications. JMIR Med Inform 2021; 9:e23898. [PMID: 34673533 PMCID: PMC8569540 DOI: 10.2196/23898] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 02/28/2021] [Accepted: 04/27/2021] [Indexed: 12/16/2022] Open
Abstract
With the rapid growth of information technology, the necessity for processing substantial amounts of health data using advanced information technologies is increasing. A large amount of valuable data exists in natural text such as diagnosis text, discharge summaries, online health discussions, and eligibility criteria of clinical trials. Health natural language processing, as an interdisciplinary field of natural language processing and health care, plays a substantial role in a wide scope of both methodology development and applications. This editorial shares the most recent methodology innovations of health natural language processing and applications in the medical domain published in this JMIR Medical Informatics special theme issue entitled "Health Natural Language Processing: Methodology Development and Applications".
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Affiliation(s)
- Tianyong Hao
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, Guangzhou, China
| | - Likeng Liang
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Heng Weng
- The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Buzhou Tang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China
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7
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Li Z, Chen H, Qi R, Lin H, Chen H. DocR-BERT: Document-level R-BERT for Chemical-induced Disease Relation Extraction via Gaussian Probability Distribution. IEEE J Biomed Health Inform 2021; 26:1341-1352. [PMID: 34591774 DOI: 10.1109/jbhi.2021.3116769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Chemical-induced disease (CID) relation extraction from biomedical articles plays an important role in disease treatment and drug development. Existing methods are insufficient for capturing complete document level semantic information due to ignoring semantic information of entities in different sentences. In this work, we proposed an effective document-level relation extraction model to automatically extract intra-/inter-sentential CID relations from articles. Firstly, our model employed BERT to generate contextual semantic representations of the title, abstract and shortest dependency paths (SDPs). Secondly, to enhance the semantic representation of the whole document, cross attention with self-attention (named cross2self-attention) between abstract, title and SDPs was proposed to learn the mutual semantic information. Thirdly, to distinguish the importance of the target entity in different sentences, the Gaussian probability distribution was utilized to compute the weights of the co-occurrence sentence and its adjacent entity sentences. More complete semantic information of the target entity is collected from all entities occurring in the document via our presented document-level R-BERT (DocR-BERT). Finally, the related representations were concatenated and fed into the softmax function to extract CIDs. We evaluated the model on the CDR corpus provided by BioCreative V. The proposed model without external resources is superior in performance as compared with other state-of-the-art models (our model achieves 53.5%, 70%, and 63.7% of the F1-score on inter-/intra-sentential and overall CDR dataset). The experimental results indicate that cross2self-attention, the Gaussian probability distribution and DocR-BERT can effectively improve the CID extraction performance. Furthermore, the mutual semantic information learned by the cross self-attention from abstract towards title can significantly influence the extraction performance of document-level biomedical relation extraction tasks.
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8
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Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In the biomedical field, building an efficient and accurate RE system is critical for the construction of a domain knowledge base to support upper-level applications. Recent advances have witnessed a focus shift from sentence to document-level RE problems, which are more challenging due to the need for inter- and intra-sentence semantic reasoning. This type of distant dependency is difficult to understand and capture for a learning algorithm. To address the challenge, prior efforts either attempted to improve the cross sentence text representation or infuse domain or local knowledge into the model. Both strategies demonstrated efficacy on various datasets. In this paper, a keyword-attentive knowledge infusion strategy is proposed and integrated into BioBERT. A domain keyword collection mechanism is developed to discover the most relation-suggestive word tokens for bio-entities in a given context. By manipulating the attention masks, the model can be guided to focus on the semantic interaction between bio-entities linked by the keywords. We validated the proposed method on the Biocreative V Chemical Disease Relation dataset with an F1 of 75.6%, outperforming the state-of-the-art by 5.6%.
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9
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Guo S, Huang L, Yao G, Wang Y, Guan H, Bai T. Extracting Biomedical Entity Relations using Biological Interaction Knowledge. Interdiscip Sci 2021; 13:312-320. [PMID: 33730356 DOI: 10.1007/s12539-021-00425-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/24/2021] [Accepted: 03/05/2021] [Indexed: 10/21/2022]
Abstract
Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be able to combine available annotated gene databases, chemical, genomic, clinical and other types of data repositories as domain knowledge to assist the extraction of biological entity relations from numerous literatures. Under this scenario, this paper proposes BioGraphSAGE model, a Siamese graph neural network with structured databases as domain knowledge to extract biological entity relations from literatures. Our model combines both biological semantic features and positional features to improve the recognition of relations between distant entities in the same literature. The experiment results show that BioGraphSAGE achieves the best F1 score among other relation extraction models on smaller annotated samples. Moreover, the proposed model can still maintain a F1 score of 0.526 without using annotated training samples.
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Affiliation(s)
- Shuyu Guo
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Gang Yao
- Department of Neurology, the Second Hospital of Jilin University, Changchun, 130012, China
| | - Ye Wang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Haotian Guan
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Tian Bai
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China. .,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.
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Zeng D, Zhao C, Quan Z. CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction. Front Genet 2021; 12:624307. [PMID: 33643385 PMCID: PMC7902761 DOI: 10.3389/fgene.2021.624307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/18/2021] [Indexed: 11/26/2022] Open
Abstract
Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the document rather than single sentence. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe limitation of the graph-based approaches is the over-smoothing problem, which decreases the model distinguishing ability. In this paper, we propose CID-GCN, an effective Graph Convolutional Networks (GCNs) with gating mechanism, for CID relation extraction. Specifically, we construct a heterogeneous graph which contains mention, sentence and entity nodes. Then, the graph convolution operation is employed to aggregate interactive information on the constructed graph. Particularly, we combine gating mechanism with the graph convolution operation to address the over-smoothing problem. The experimental results demonstrate that our approach significantly outperforms the baselines.
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Affiliation(s)
- Daojian Zeng
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
| | - Chao Zhao
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
| | - Zhe Quan
- College of Information Science and Engineering, Hunan University, Changsha, China
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