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LivingNER corpus: Named entity recognition, normalization & classification of species, pathogens and food ...
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LivingNER corpus: Named entity recognition, normalization & classification of species, pathogens and food ...
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DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases ...
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DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases ...
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Deep Learning with Word Embedding Improves Kazakh Named-Entity Recognition
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In: Information; Volume 13; Issue 4; Pages: 180 (2022)
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Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 4 (2022)
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Creating Biographical Networks from Chinese and English Wikipedia
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In: https://halshs.archives-ouvertes.fr/halshs-03217972 ; 2021 (2021)
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Sentiment Analysis of Arabic Documents
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In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
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WEIR-P: An Information Extraction Pipeline for the Wastewater Domain
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In: RCIS 2021 - 5th International Conference on Research Challenges in Information Science ; https://hal.archives-ouvertes.fr/hal-03211461 ; RCIS 2021 - 5th International Conference on Research Challenges in Information Science, May 2021, Virtual, Cyprus (2021)
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Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks
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In: Mathematics; Volume 9; Issue 24; Pages: 3319 (2021)
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Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora
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In: ISSN: 2504-0537 ; Frontiers in research metrics and analytics, Vol. 6 (2021) P. 689803 (2021)
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Abstract:
The health and life science domains are well known for their wealth of named entities found in large free text corpora, such as scientific literature and electronic health records. To unlock the value of such corpora, named entity recognition (NER) methods are proposed. Inspired by the success of transformer-based pretrained models for NER, we assess how individual and ensemble of deep masked language models perform across corpora of different health and life science domains-biology, chemistry, and medicine-available in different languages-English and French. Individual deep masked language models, pretrained on external corpora, are fined-tuned on task-specific domain and language corpora and ensembled using classical majority voting strategies. Experiments show statistically significant improvement of the ensemble models over an individual BERT-based baseline model, with an overall best performance of 77% macro F1-score. We further perform a detailed analysis of the ensemble results and show how their effectiveness changes according to entity properties, such as length, corpus frequency, and annotation consistency. The results suggest that the ensembles of deep masked language models are an effective strategy for tackling NER across corpora from the health and life science domains.
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Keyword:
Chemical patents; Clinical NER; Clinical text mining; Deep learning; info:eu-repo/classification/ddc/025.063; info:eu-repo/classification/ddc/616.0757; Named entity recognition; Patent text mining; Transformers; Wet lab protocols
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URL: https://archive-ouverte.unige.ch/unige:157989
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The Comparison Between the Tools for Named Entity Recognition
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French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
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In: CMLC-8 - 8th Workshop on the Challenges in the Management of Large Corpora ; https://hal.inria.fr/hal-02678358 ; CMLC-8 - 8th Workshop on the Challenges in the Management of Large Corpora, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/CMLC-8book.pdf (2020)
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UNER: Universal Named-Entity Recognition Framework ...
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Alves, Diego. - : Leibniz Universität Hannover (LUH),L3S Research Center,CLEOPATRA ITN, 2020
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Cantemist guidelines: neoplasms morphology annotation and mapping to CIEO-3 ...
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Cantemist guidelines: neoplasms morphology annotation and mapping to CIEO-3 ...
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Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology ...
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Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology ...
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Cantemist guidelines: neoplasms morphology annotation and mapping to CIEO-3 ...
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