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- W3005514250 abstract "Interactions among microorganisms have been the key to understand microbial communities. As an important member of microorganisms, bacteria are closely related to human diseases. Therefore, studying the interaction between bacteria plays an important role in microbiome research. There are a large number of published medical literatures that contain small-scale data about the interactions between bacteria. These literatures often record the bacteria interactions discovered by co-cultural experiments for two or more species. Mining and organizing them into databases will provide reliable support for microbiome research. Named entity recognition (NER) is an essential step of interaction extraction (IE) by automatically identifying bacterial entities in the text. In this paper, we propose a method based on language model for identifying bacteria named entities. Using the language model to learn the semantic information between words, the F1 score reaches 96.14%, which is the best performance in bacteria NER compared with the previous experimental results." @default.
- W3005514250 created "2020-02-14" @default.
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- W3005514250 date "2019-11-01" @default.
- W3005514250 modified "2023-09-25" @default.
- W3005514250 title "Bacterial Named Entity Recognition Based on Language Model" @default.
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- W3005514250 doi "https://doi.org/10.1109/bibm47256.2019.8983133" @default.
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