Natural Language Processing'Natural-language processing' ('NLP') is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.
IEEE Transactions on Knowledge and Data Engineering, pp.1-1, (2020)
Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language applications such as question ans...
Cited by36BibtexViews85Links
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Ji Shaoxiong,Pan Shirui, Cambria Erik, Marttinen Pekka, Yu Philip S.
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive ...
Cited by5BibtexViews839Links
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Amy J.C. Trappey,Charles V. Trappey, Jheng-Long Wu, Jack W.C. Wang
ADVANCED ENGINEERING INFORMATICS, (2020): 101027
Patents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to und...
Cited by2BibtexViews27Links
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Katikapalli Subramanyam Kalyan, S. Sangeetha
arXiv: Computation and Language, (2020): 103323
Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture the prior know...
Cited by2BibtexViews42Links
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The paper surveys evaluation methods of natural language generation (NLG) systems that have been developed in the last few years. We group NLG evaluation methods into three categories: (1) human-centric evaluation metrics, (2) automatic metrics that require no training, and (3)...
Cited by1BibtexViews41Links
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Han Xu, Gao Tianyu,Lin Yankai, Peng Hao, Yang Yaoliang, Xiao Chaojun,Liu Zhiyuan,Li Peng,Sun Maosong,Zhou Jie
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing ...
Cited by1BibtexViews120Links
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Qiu Xipeng, Sun Tianxiang, Xu Yige, Shao Yunfan, Dai Ning,Huang Xuanjing
Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we sys...
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Jifan Yu,Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang,Wenzheng Feng, Junyi Luo, Chenyu Wang,Lei Hou,Juanzi Li,Zhiyuan Liu,Jie Tang
ACL, pp.3135-3142, (2020)
We present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource
Cited by0BibtexViews395Links
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ACL, pp.4902-4912, (2020)
Adopting principles from behavioral testing in software engineering, we propose CheckList, a model-agnostic and task-agnostic testing methodology that tests individual capabilities of the model using three different test types
Cited by0BibtexViews1140Links
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Hongyeon Yu, Jaehyun An, Jeongmin Yoon, Hyemin Kim,Youngjoong Ko
Computer Speech & Language, (2020): 91-113
Cited by0BibtexViews37Links
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Andrea Galassi,Marco Lippi,Paolo Torroni
IEEE transactions on neural networks and learning systems, (2020)
Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this ar...
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Cristina Garbacea,Qiaozhu Mei
Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. Neural architectures have been employed to generate text excerpts to various deg...
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Minaee Shervin, Kalchbrenner Nal, Cambria Erik, Nikzad Narjes, Chenaghlu Meysam, Gao Jianfeng
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this work, we provide a detailed review of more ...
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Chien-Hua Chen,Jer-Guang Hsieh, Shu-Ling Cheng,Yih-Lon Lin, Po-Hsiang Lin,Jyh-Horng Jeng
The American journal of emergency medicine, (2020)
NLP-based models can be used as an early short-term prediction of LOS and have the potential for mixed-type clinical data analysis. The proposed models would likely aid ED physicians' decision-making processes and improve ED quality of care.
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Florian Jungmann, Benedikt Kämpgen, Philipp Mildenberger,Igor Tsaur, Tobias Jorg,Christoph Düber,Peter Mildenberger,Roman Kloeckner
Int. J. Medical Informatics, (2020): 104106
Applying this NLP approach to already existing free-text reports allows the conversion of such reports into a structured form. This may be valuable for epidemiological studies, to evaluate the appropriateness of CT examinations, or to answer a variety of research questions.
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Thomas Weikert, Ivan Nesic, Joshy Cyriac,Jens Bremerich,Alexander W Sauter,Gregor Sommer,Bram Stieltjes
European journal of radiology, (2020): 108862
Our NLP-based approaches allow for an automated and highly accurate retrospective classification of CTPA reports with manageable effort solely using unstructured impression sections. We demonstrated that this approach is useful for the classification of radiology reports not writ...
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Rogers Anna, Kovaleva Olga,Rumshisky Anna
Transformer-based models are now widely used in NLP, but we still do not understand a lot about their inner workings. This paper describes what is known to date about the famous BERT model (Devlin et al. 2019), synthesizing over 40 analysis studies. We also provide an overview ...
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Vera Sorin, Yiftach Barash,Eli Konen,Eyal Klang
Journal of the American College of Radiology : JACR, (2020)
Research and use of deep learning NLP in radiology is increasing. Acquaintance with this technology can help prepare radiologists for the coming changes in their field.
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Journal of pain and symptom management, (2020)
Care preference documentation within 48 hours was absent in over one-third of ICU admissions among patients aged ≥75 years and was more likely to occur in medical versus cardiac or surgical ICUs.
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Keywords
Natural Language ProcessingMachine LearningElectronic Health RecordsEpidemiologyArtificial IntelligenceInformation ExtractionText MiningClinical NotesElectronic Health RecordConditional Random Field
Authors
Jie Tang
Paper 8
Juanzi Li
Paper 6
Hongfang Liu
Paper 4
Sunghwan Sohn
Paper 3
Steven J. Jacobsen
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Gianni Pantaleo
Paper 2
Curtis P. Langlotz
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Steven G. Coca
Paper 2
Lina S. Sy
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Charlotta Lindvall
Paper 2