Relevant bibliographies by topics / Drug named entity recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Drug named entity recognition'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Drug named entity recognition"

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Korkontzelos, Ioannis, Dimitrios Piliouras, Andrew W. Dowsey, and Sophia Ananiadou. "Boosting drug named entity recognition using an aggregate classifier." Artificial Intelligence in Medicine 65, no. 2 (2015): 145–53. http://dx.doi.org/10.1016/j.artmed.2015.05.007.

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T., Mathu, and Raimond Kumudha. "A novel deep learning architecture for drug named entity recognition." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1884–91. https://doi.org/10.12928/telkomnika.v19i6.21667.

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Drug named entity recognition (DNER) becomes the prerequisite of other medical relation extraction systems. Existing approaches to automatically recognize drug names includes rule-based, machine learning (ML) and deep learning (DL) techniques. DL techniques have been verified to be the state-of-the-art as it is independent of handcrafted features. The previous DL methods based on word embedding input representation uses the same vector representation for an entity irrespective of its context in different sentences and hence could not capture the context properly. Also, identification of the n-
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Mathu, T., and Kumudha Raimond. "A novel deep learning architecture for drug named entity recognition." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 6 (2021): 1884. http://dx.doi.org/10.12928/telkomnika.v19i6.21667.

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Kang, Keming, Shengwei Tian, and Long Yu. "Named entity recognition of local adverse drug reactions in Xinjiang based on transfer learning." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 8899–914. http://dx.doi.org/10.3233/jifs-201017.

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For deep learning’s insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data se
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Xiong, Wangping, Jun Cao, Xian Zhou, et al. "Design and Evaluation of a Prescription Drug Monitoring Program for Chinese Patent Medicine based on Knowledge Graph." Evidence-Based Complementary and Alternative Medicine 2021 (July 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/9970063.

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Background. Chinese patent medicines are increasingly used clinically, and the prescription drug monitoring program is an effective tool to promote drug safety and maintain health. Methods. We constructed a prescription drug monitoring program for Chinese patent medicines based on knowledge graphs. First, we extracted the key information of Chinese patent medicines, diseases, and symptoms from the domain-specific corpus by the information extraction. Second, based on the extracted entities and relationships, a knowledge graph was constructed to form a rule base for the monitoring of data. Then
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Wang, Qi, and Xiyou Su. "Research on Named Entity Recognition Methods in Chinese Forest Disease Texts." Applied Sciences 12, no. 8 (2022): 3885. http://dx.doi.org/10.3390/app12083885.

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Named entity recognition of forest diseases plays a key role in knowledge extraction in the field of forestry. The aim of this paper is to propose a named entity recognition method based on multi-feature embedding, a transformer encoder, a bi-gated recurrent unit (BiGRU), and conditional random fields (CRF). According to the characteristics of the forest disease corpus, several features are introduced here to improve the method’s accuracy. In this paper, we analyze the characteristics of forest disease texts; carry out pre-processing, labeling, and extraction of multiple features; and construc
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Meenachisundaram, Thiyagu, and Manjula Dhanabalachandran. "Biomedical Named Entity Recognition Using the SVM Methodologies and bio Tagging Schemes." Revista de Chimie 72, no. 4 (2021): 52–64. http://dx.doi.org/10.37358/rc.21.4.8456.

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Biomedical Named Entity Recognition (BNER) is identification of entities such as drugs, genes, and chemicals from biomedical text, which help in information extraction from the domain literature. It would allow extracting information such as drug profiles, similar or related drugs and associations between drugs and their targets. This venue presents opportunities for improvement even though many machine learning methods have been applied. The efficiency can be improved in case of biological related chemical entities as there are varied structure and properties. This new approach combines two s
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Liang, Jun, Xuemei Xian, Xiaojun He, et al. "A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text." Journal of Healthcare Engineering 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/4898963.

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Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-b
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Azhar, Daris, Robert Kurniawan, Waris Marsisno, Budi Yuniarto, Sukim Sukim, and Sugiarto Sugiarto. "Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 375–82. https://doi.org/10.11591/ijai.v13.i1.pp375-382.

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The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) architecture because it can pro
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Azhar, Daris, Robert Kurniawan, Waris Marsisno, Budi Yuniarto, Sukim Sukim, and Sugiarto Sugiarto. "Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 375. http://dx.doi.org/10.11591/ijai.v13.i1.pp375-382.

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<span lang="EN-US">The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) archit
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Dissertations / Theses on the topic "Drug named entity recognition"

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Benajiba, Yassine. "Arabic named entity recognition." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8318.

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En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimi
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MENEZES, DANIEL SPECHT SILVA. "NAMED ENTITY RECOGNITION FOR PORTUGUESE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35855@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>BOLSA NOTA 10<br>A produção e acesso a quantidades imensas dados é um elemento pervasivo da era da informação. O volume de informação disponível é sem precedentes na história da humanidade e está sobre constante processo de expansão. Uma oportunidade que emerge neste ambiente é o desenvolvimento de aplicações que sejam capazes de estruturar conhecimento contido nesses dados. Neste co
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Alotaibi, Fahd Saleh S. "Fine-grained Arabic named entity recognition." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5970/.

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This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly in infection process. Instead of classifying the detected NE phrases into small sets of classes, we target a broader range (i.e. 50 fine-grained classes 'hierarchal-based of two levels') to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in
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Sun, Bowen. "Named entity recognition : Evaluation of Existing Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11223.

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Nowadays, one subfield of information extraction, Named Entity Recognition, becomes more and more important. It helps machine to recognize proper nouns (entities) in text and associating them with the appropriate types. Common types in NER systems are location, person name, date, address, etc. There are several NER systems in the world. What‘s the main core technology of these systems? Which kind of system is better? How to improve this technology in the future? This master thesis will show the basic and detail knowledge about NER.Three existing NER systems will be choose to evaluate in this p
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MICKELIN, JOEL. "Named Entity Recognition with Support Vector Machines." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138012.

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This report describes a degree project in Computer Science, the aim of which was to construct a system for Named Entity Recognition in Swedish texts of names of people, locations and organizations, as well as expressions for time. This system was constructed from the part-of-speech tagger Granska and the Support Vector Machine system SVMlin. The completed system was trained to recognize Named Entities by analyzing patterns in training corpora consisting of lists of example words belonging to each category. The system was initially trained to recognize patterns based on individual characters in
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Aljic, Almir, and Theodor Kraft. "Contextualising government reports using Named Entity Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281835.

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The science of making a computer understand text and process it, natural language processing, is a topic of great interest among researchers. This study aims to further that research by comparing the BERT algorithm and classic logistic regression when identifying names of public organizations. The results show that BERT outperforms its competitor in the task from the data which consisted of public state inquiries and reports. Furthermore a literature study was conducted as a way of exploring how a system for NER can be implemented into the management of an organization. The study found that th
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Zhang, Yaxi. "Named Entity Recognition for Social Media Text." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395978.

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This thesis aims to perform named entity recognition for English social media texts. Named Entity Recognition (NER) is applied in many NLP tasks as an important preprocessing procedure. Social media texts contain lots of real-time data and therefore serve as a valuable source for information extraction. Nevertheless, NER for social media texts is a rather challenging task due to the noisy context. Traditional approaches to deal with this task use hand-crafted features but prove to be both time-consuming and very task-specific. As a result, they fail to deliver satisfactory performance. The goa
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Traboulsi, Hayssam N. "Named entity recognition : a local grammar-based approach." Thesis, University of Surrey, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431104.

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Alasiry, Areej Mohammed. "Named entity recognition and classification in search queries." Thesis, Birkbeck (University of London), 2015. http://bbktheses.da.ulcc.ac.uk/154/.

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Named Entity Recognition and Classification is the task of extracting from text, instances of different entity classes such as person, location, or company. This task has recently been applied to web search queries in order to better understand their semantics, where a search query consists of linguistic units that users submit to a search engine to convey their search need. Discovering and analysing the linguistic units comprising a search query enables search engines to reveal and meet users' search intents. As a result, recent research has concentrated on analysing the constituent units com
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Algahtani, Shabib Mallouh. "Arabic named entity recognition : a corpus-based study." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/arabic-named-entity-recognition-a-corpusbased-study(6d7bbbd0-c2eb-4e6a-8ba5-b370f5c8d0e5).html.

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The task of finding and classifying proper nouns in natural language text is the core of most Named Entity Recognition (NER) systems. The NER problem has received much attention, as NER forms the basic building block of any Information Extraction system. Although finding and classifying proper nouns in text is a very challenging task in English, the task benefits a great deal from the distinguishing orthographic feature of capitalization. When this feature is missing, as in uppercase text, or is present at the start of a sentence, ambiguity increases, and requires more knowledge sources to res
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Books on the topic "Drug named entity recognition"

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Zhong, Xiaoshi, and Erik Cambria. Time Expression and Named Entity Recognition. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78961-9.

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Guo, Shuli, Lina Han, and Wentao Yang. Clinical Chinese Named Entity Recognition in Natural Language Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2665-7.

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Cambria, Erik, and Xiaoshi Zhong. Time Expression and Named Entity Recognition. Springer International Publishing AG, 2022.

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Yang, Wentao. Clinical Chinese Named Entity Recognition in Natural Language Processing. Springer, 2023.

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Fridlund, Mats, Mila Oiva, and Petri Paju, eds. Digital Histories: Emergent Approaches within the New Digital History. Helsinki University Press, 2020. http://dx.doi.org/10.33134/hup-5.

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Historical scholarship is currently undergoing a digital turn. All historians have experienced this change in one way or another, by writing on word processors, applying quantitative methods on digitalized source materials, or using internet resources and digital tools. Digital Histories showcases this emerging wave of digital history research. It presents work by historians who – on their own or through collaborations with e.g. information technology specialists – have uncovered new, empirical historical knowledge through digital and computational methods. The topics of the volume range from
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Book chapters on the topic "Drug named entity recognition"

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Sreejith Panickar, Suja, Lisa Verma, Riya Agrawal, Vaidehi Jadhav, and Sakshi Gupta. "Creating Drugs Related Corpora for Efficient Drug Named Entity Recognition." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1452-3_23.

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Xia, Long, G. Alan Wang, and Weiguo Fan. "A Deep Learning Based Named Entity Recognition Approach for Adverse Drug Events Identification and Extraction in Health Social Media." In Smart Health. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67964-8_23.

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Mohit, Behrang. "Named Entity Recognition." In Natural Language Processing of Semitic Languages. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_7.

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Béchet, Frédéric. "Named Entity Recognition." In Spoken Language Understanding. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119992691.ch10.

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dos Santos, Cícero Nogueira, and Ruy Luiz Milidiú. "Named Entity Recognition." In Entropy Guided Transformation Learning: Algorithms and Applications. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2978-3_7.

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Hakenberg, Jörg. "Named Entity Recognition." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_155.

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Devarakonda, Murthy V., Kalpana Raja, and Hua Xu. "Named Entity Recognition." In Cognitive Informatics in Biomedicine and Healthcare. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55865-8_4.

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Yeniterzi, Reyyan, Gökhan Tür, and Kemal Oflazer. "Turkish Named-Entity Recognition." In Turkish Natural Language Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90165-7_6.

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Nouvel, Damien, Maud Ehrmann, and Sophie Rosset. "Evaluating Named Entity Recognition." In Named Entities for Computational Linguistics. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119268567.ch6.

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Lopez, Cédric, Melissa Mekaoui, Kevin Aubry, et al. "Recursive Named Entity Recognition." In Advances in Knowledge Discovery and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90287-2_2.

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Conference papers on the topic "Drug named entity recognition"

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Le-Duc, Khai, David Thulke, Hung-Phong Tran, et al. "Medical Spoken Named Entity Recognition." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-industry.59.

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Kairatuly, Bauyrzhan, and Madina Mansurova. "Named Entity Recognition from Kazakh Speech." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773546.

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Savaram, Padmaja, Suhana Tabassum, Sasidhar Bandu, and Lakshitha B. "Multilingual Approaches to Named Entity Recognition." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866005.

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Ding, Guohui, Wenjing Tang, and Zhaoyi Yuan. "Entity Label-Guided Graph Fusion Multi-Modal Named Entity Recognition." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831708.

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Piliouras, Dimitrios, Ioannis Korkontzelos, Andrew Dowsey, and Sophia Ananiadou. "Dealing with Data Sparsity in Drug Named Entity Recognition." In 2013 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2013. http://dx.doi.org/10.1109/ichi.2013.9.

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Khedkar, Vijayshri, Devshi Desai, Sonali Kothari Tidke, Charlotte Fernandes, and Mansi R. "Chemical Named Entity Recognition for Ovarian Cancer’s Drug Discovery." In 2022 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2022. http://dx.doi.org/10.1109/dasa54658.2022.9765001.

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Li, Wei-Yan, Wen-Ai Song, Xin-Hong Jia, et al. "Drug Specification Named Entity Recognition Base on BiLSTM-CRF Model." In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2019. http://dx.doi.org/10.1109/compsac.2019.10244.

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ZHONG, SHANHAO, and QINGSONG YU. "IMPROVING CHINESE MEDICAL NAMED ENTITY RECOGNITION USING GLYPH AND LEXICON." In 2021 INTERNATIONAL CONFERENCE ON ADVANCED EDUCATION AND INFORMATION MANAGEMENT (AEIM 2021). Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/dtssehs/aeim2021/35969.

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Abstract. Medical named entity recognition is the first step in processing electronic medical records. It is the basis for processing medical natural language text information into medical structured information, which has extremely high research value and application value. In this paper, we have proposed a model that aims to identify various types of named entities such as disease, imaging examination, laboratory examination, operation, drug, and anatomy from Chinese electronic medical record. We construct a fusion Glyph and lexicon model based on BERT. Experimental studies have shown that i
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Wang, Duzhuang, Runzhi Li, Jing Wang, and Zhanbo Li. "Chinese Drug Information Named Entity Recognition Based on MSCNN And BERT-BiLSTM-CRF." In CCRIS'22: 2022 3rd International Conference on Control, Robotics and Intelligent System. ACM, 2022. http://dx.doi.org/10.1145/3562007.3562034.

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Wunnava, Susmitha, Xiao Qin, Tabassum Kakar, Xiangnan Kong, and Elke Rundensteiner. "A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events." In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.306.

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Reports on the topic "Drug named entity recognition"

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Florian, Radu. Named Entity Recognition as a House of Cards: Classifier Stacking. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada459582.

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Elmann, Anat, Orly Lazarov, Joel Kashman, and Rivka Ofir. therapeutic potential of a desert plant and its active compounds for Alzheimer's Disease. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7597913.bard.

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We chose to focus our investigations on the effect of the active forms, TTF and AcA, rather than the whole (crude) extract. 1. To establish cultivation program designed to develop lead cultivar/s (which will be selected from the different Af accessions) with the highest yield of the active compounds TTF and/or achillolide A (AcA). These cultivar/s will be the source for the purification of large amounts of the active compounds when needed in the future for functional foods/drug development. This task was completed. 2. To determine the effect of the Af extract, TTF and AcA on neuronal vulnerabi
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