Named entity recognition and relation detection for biomedical information extraction
The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
[HTML][HTML] Neural network-based approaches for biomedical relation classification: a review
The explosive growth of biomedical literature has created a rich source of knowledge, such
as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in …
as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in …
Reasoning with latent structure refinement for document-level relation extraction
Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …
multiple sentences of a document and capturing complex interactions between inter …
Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction
Abstract Entities, as the essential elements in relation extraction tasks, exhibit certain
structure. In this work, we formulate such entity structure as distinctive dependencies …
structure. In this work, we formulate such entity structure as distinctive dependencies …
Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Document-level relation extraction is a complex human process that requires logical
inference to extract relationships between named entities in text. Existing approaches use …
inference to extract relationships between named entities in text. Existing approaches use …
Global-to-local neural networks for document-level relation extraction
Relation extraction (RE) aims to identify the semantic relations between named entities in
text. Recent years have witnessed it raised to the document level, which requires complex …
text. Recent years have witnessed it raised to the document level, which requires complex …
Biomedical relation extraction via knowledge-enhanced reading comprehension
Background In biomedical research, chemical and disease relation extraction from
unstructured biomedical literature is an essential task. Effective context understanding and …
unstructured biomedical literature is an essential task. Effective context understanding and …
[HTML][HTML] Character level and word level embedding with bidirectional LSTM–Dynamic recurrent neural network for biomedical named entity recognition from literature
Abstract Named Entity Recognition is the process of identifying different entities in a given
context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical …
context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical …
Convolution neural network for text mining and natural language processing
NI Widiastuti - IOP Conference Series: Materials Science and …, 2019 - iopscience.iop.org
The objective of this study is to get an overview of the improvements applied in a number of
studies and problems that have not been resolved. We have surveyed more than 30 …
studies and problems that have not been resolved. We have surveyed more than 30 …
[HTML][HTML] Triple Pseudo-Siamese network with hybrid attention mechanism for welding defect detection
Z Li, H Chen, X Ma, H Chen, Z Ma - Materials & Design, 2022 - Elsevier
Most of the existing methods neglect their complementary relation and only use welding
pool images to detect welding defects. Therefore, a new triple pseudo-siamese network to …
pool images to detect welding defects. Therefore, a new triple pseudo-siamese network to …