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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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Genetic Neural Architecture Search for automatic assessment of human sperm images
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In: ISSN: 0957-4174 ; Expert Systems with Applications ; https://hal.archives-ouvertes.fr/hal-03585035 ; Expert Systems with Applications, Elsevier, 2022 (2022)
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| 3 |
Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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| 5 |
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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| 7 |
Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
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In: Electronics; Volume 11; Issue 3; Pages: 374 (2022)
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| 8 |
COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset
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In: Healthcare; Volume 10; Issue 3; Pages: 411 (2022)
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| 9 |
A Novel Pathological Voice Identification Technique through Simulated Cochlear Implant Processing Systems
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2398 (2022)
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| 10 |
Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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| 11 |
Multimodal Lip-Reading for Tracheostomy Patients in the Greek Language
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In: Computers; Volume 11; Issue 3; Pages: 34 (2022)
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| 12 |
Identifying Learners’ Interaction Patterns in an Online Learning Community
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 4; Pages: 2245 (2022)
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| 13 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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Abstract:
The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora.
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Keyword:
adverse drug events; annotated corpus; coreference relation extraction; deep learning; information extraction; language models; machine learning; MESHRUS; named entity recognition; neural networks; pharmacovigilance; social media; UMLS
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URL: https://doi.org/10.3390/app12010491
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| 14 |
An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases
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In: Diagnostics; Volume 12; Issue 4; Pages: 836 (2022)
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| 15 |
Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going?
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In: Diagnostics; Volume 12; Issue 4; Pages: 927 (2022)
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| 16 |
Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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| 17 |
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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| 18 |
The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China
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In: Sustainability; Volume 14; Issue 5; Pages: 2923 (2022)
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| 19 |
Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review
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In: Diagnostics; Volume 12; Issue 4; Pages: 874 (2022)
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| 20 |
Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks
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In: Micromachines; Volume 13; Issue 4; Pages: 501 (2022)
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