Results 211 to 220 of about 379,251 (377)

Ontology Attention Layer for Medical Named Entity Recognition

Applied Sciences
Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field.
Yue Zha, Yuanzhi Ke, Xiao Hu, Caiquan Xiong   +3 more
doaj   +1 more source

DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition [PDF]

arXiv
End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on phonetic-level
arxiv  

Clonal Hematopoiesis and Thrombosis

American Journal of Hematology, EarlyView.
ABSTRACT Clonal hematopoiesis (CH) has been the focus of many research efforts in the last years and has emerged as a risk modifier for cardiovascular disease morbidity and mortality. While substantial evidence has accumulated regarding its impact on arterial system diseases, the connection with venous thrombosis has only recently been explored.
Cornelia Englisch, Cihan Ay
wiley   +1 more source

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition [PDF]

, 2006
Richard Tzong‐Han Tsai, Cheng-Lung Sung, Hong-Jie Dai, Hsieh-Chuan Hung, Ting-Yi Sung, Wen-Lian Hsu   +5 more
openalex   +1 more source

A Hidden Markov Model Based Named Entity Recognition System: Bengali and Hindi as Case Studies [PDF]

, 2007
Asif Ekbal, Sivaji Bandyopadhyay
openalex   +1 more source

Chemical Process Development in the Pharmaceutical Industry in Europe—Insights and Perspectives from Industry Scientists

Angewandte Chemie, EarlyView.
What are the tasks of chemical process development in the pharmaceutical industry? What are trends and challenges? This Scientific Perspective written by members of the European pharmaceutical industry introduces key concepts of drug development and key drivers for the future of chemical process development.
Joachim Krueger, André P. Dieskau, Jorma Hassfeld, Joerg Gries, Oliver Block, Hilmar Weinmann, Daniel Kaufmann, Stefan Hildbrand, Volker Kraft, Robert Moeckel, Juan R. Dehli, Ulrich Scholz, Carl Friedrich Nising   +12 more
wiley   +2 more sources

TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction

Intelligent Systems with Applications
Tourism named entity recognition is indispensable in tourism information extraction, and plays a crucial role in constructing tourism knowledge map and enhancing tourism knowledge quiz system.
Kai Gao, Jiahao Zhou, Yunxian Chi, Yimin Wen   +3 more
doaj  

Cascaded classifiers for confidence-based chemical named entity recognition [PDF]

, 2008
Peter Corbett, Ann Copestake
openalex   +1 more source

Iridium(III) Carbene Complexes Featuring Either Metal‐to‐Ligand Charge Transfer (MLCT) or Through‐Space Charge Transfer (TSCT) Blue Luminescence

Angewandte Chemie, EarlyView.
Ir(III) complexes with contrasting MLCT or TSCT transition characters were synthesized, among which the TSCT emitter f‐ct13b gave PhOLED device with an EQEmax of 22.2% and CIExy of (0.155, 0.120). Additionally, hyper‐OLED bearing another TSCT sensitizer, f‐ct13c and v‐DABNA terminal emitter, achieved a narrowband blue hyperphosphorescence with EQEmax ...
Jie Yan, Yixin Wu, Manli Huang, Lin Cheng, Yi Pan, Chi‐Chi Wu, Chia‐Hsun Yeh, Jian‐Liang Li, Yan‐Ding Lin, Yun Chi, Chuluo Yang, Pi‐Tai Chou, Kai Chung Lau   +12 more
wiley   +2 more sources

Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition [PDF]

arXiv
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descriptions of each entity type: the common label PER might, for example, be verbalized as ''person entity.'' In an initial label interpretation learning phase, the model learns ...
arxiv