Improving biomedical named entity recognition by dynamic caching inter-sentence information.
Bioinformatics
; 38(16): 3976-3983, 2022 08 10.
Article
in En
| MEDLINE
| ID: mdl-35758612
ABSTRACT
MOTIVATION Biomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems. RESULTS:
We propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines. AVAILABILITYAND IMPLEMENTATION Code will be available at https//github.com/zgzjdx/BioNER-Cache. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Data Mining
/
Language
Type of study:
Prognostic_studies
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2022
Document type:
Article
Affiliation country:
China