MOTIVATION: Information about bacteria biotopes (BB) is important for fundamental research and applications in microbiology. BB task at BioNLP-OST 2019 focuses on the extraction of locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts. The subtask BB-rel+ner aims to recognize relevant entities and extract interrelationships about BBs. The corresponding corpus owns some distinctive features (e.g. nested entities) which are challenging to deal with. Therefore, previous methods achieved low performance on entity and relation extraction and limited the mutual effect between named entity recognition and relation extraction. There is still much room for improvement. RESULTS: We propose a span-based model to extract entities and relations jointly from biomedical text regarding the BBs. For alleviating the problem of annotated data deficiency in domain-specific task, we employ a BERT (Bidirectional Encoder Representations from Transformers) model pre-trained on the domain-specific corpus to encode sentences. Our model considers all spans in a sentence as potential entity mentions and computes relation scores between the most confident entity spans based on representations of spans and contexts between spans. Experiments on the BB-rel+ner 2019 corpus demonstrate that our model achieves significantly better performance than the state-of-the-art method, with a reduction of 21.6% slot error rate (SER) for extracting relations. Our model is also effective in recognizing nested entities. Furthermore, the model can be applied to the CHEMPROT corpus for joint extraction of chemical-protein entities and relations, achieving state-of-the-art performance. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/zmmzGitHub/SpanMB_BERT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets. RESULTS: We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/zmmzGitHub/MTL-BC-LBC-BioNER and all datasets are publicly available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.