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Utilizing deep learning for data mining in biomedicine often falls short in leveraging prior knowledge and adapting to the complexities of biomedical literature mining. Entity recognition, a fundamental task in information extraction, also provides data support for Natural Language Processing (NLP) downstream tasks. Bovine Viral Diarrhea Virus (BVDV) results in considerable economic losses in the cattle industry due to calf diarrhea, bovine respiratory syndrome, and cow abortion. This study aims to extract information on BVDV from relevant literature and build a knowledge base. It enhances feature extraction in the BioBERT pre-trained model using the Machine Reading Comprehension (MRC) framework for information fusion and bi-directionally extracts corpus information through the Bi-LSTM network, followed by a CRF layer for decoding and prediction. The results show the construction of a BVDV Corpus with 22 biomedical entities and introduce the BioBERT-Bi-LSTM-CRF Integrated with MRC (BBCM) model for Named Entity Recognition (NER), combining prior knowledge and the reading comprehension framework (MRC). The BBCM model achieves $F1$-scores of 78.79% and 76.3% on the public datasets JNLPBA and GENIA, respectively, and 67.52% on the BVDV Corpus, outperforming other models. This research presents a targeted NER method for BVDV, effectively identifying related entities and exploring their relationships, thus providing valuable data support for NLP’s downstream tasks.

The primary biomedical literature is being generated at an unprecedented rate, and researchers cannot keep abreast of new developments in their fields. Biomedical natural language processing is being developed to address this issue, but building reliable systems often requires many expert-hours. We present an approach for automatically developing collections of regular expressions to drive high-performance concept recognition systems with minimal human interaction. We applied our approach to develop MutationFinder, a system for automatically extracting mentions of point mutations from the text. MutationFinder achieves performance equivalent to or better than manually developed mutation recognition systems, but the generation of its 759 patterns has required only 5.5 expert-hours. We also discuss the development and evaluation of our recently published high-quality, human-annotated gold standard corpus, which contains 1,515 complete point mutation mentions annotated in 813 abstracts. Both MutationFinder and the complete corpus are publicly available at .

A common difficulty in the annotation of large corpora is the disambiguation of the meanings of polysemous words, a task which currently depends on unassisted manual checking by human experts. In this paper, we propose RFR-SUM, an approach to the automatic disambiguation of polysemous Chinese words that calculates and then sums the collocational strengths of words in the local context of a given word. The approach is efficient in that it learns from a small annotated corpus. It can also be used to support manual checking by automatically identifying sentences where usages are borderline and require a human decision. Testing has shown that RFR-SUM outperforms two other commonly used WSD approaches.