Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBM47256.2019.8983133 | BIBM |
Field | DocType | Citations |
F1 score,Text mining,On Language,Computer science,Microbial interaction,Microbiome,Semantic information,Artificial intelligence,Natural language processing,Named-entity recognition,Language model,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xusheng Li | 1 | 0 | 1.01 |
| Chengcheng Fu | 2 | 0 | 1.35 |
| Ran Zhong | 3 | 0 | 1.69 |
| Duo Zhong | 4 | 0 | 1.69 |
| Tingting He | 5 | 348 | 61.04 |
| Xingpeng Jiang | 6 | 34 | 20.30 |