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Pre-trained language models in biomedical domain: A systematic survey
Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …
language processing tasks. This also benefits the biomedical domain: researchers from …
Data-driven materials research enabled by natural language processing and information extraction
Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …
society, but particularly in the scientific domain, there is increased importance placed on …
ScispaCy: fast and robust models for biomedical natural language processing
Despite recent advances in natural language processing, many statistical models for
processing text perform extremely poorly under domain shift. Processing biomedical and …
processing text perform extremely poorly under domain shift. Processing biomedical and …
Construction of the literature graph in semantic scholar
We describe a deployed scalable system for organizing published scientific literature into a
heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting …
heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting …
Autonomous discovery in the chemical sciences part I: Progress
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …
discovery in the chemical sciences. In this first part, we describe a classification for …
ASRNN: A recurrent neural network with an attention model for sequence labeling
Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …
labeling plays an important role by automatically analyzing a sequence (text) to assign …
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation In biomedical research, chemical is an important class of entities, and chemical
named entity recognition (NER) is an important task in the field of biomedical information …
named entity recognition (NER) is an important task in the field of biomedical information …
ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature
The emergence of “big data” initiatives has led to the need for tools that can automatically
extract valuable chemical information from large volumes of unstructured data, such as the …
extract valuable chemical information from large volumes of unstructured data, such as the …
Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks
Objective Most existing fine-tuned biomedical large language models (LLMs) focus on
enhancing performance in monolingual biomedical question answering and conversation …
enhancing performance in monolingual biomedical question answering and conversation …
Autonomous discovery in the chemical sciences part II: outlook
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this second part, we reflect on a selection of …
discovery in the chemical sciences. In this second part, we reflect on a selection of …