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Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

Transfer learning for biomedical named entity recognition with neural networks.

John M Giorgi^1,2, Gary D Bader^1,2,3

¹Department of Computer Science, University of Toronto, Toronto, Canada.

Bioinformatics (Oxford, England)

|June 6, 2018

Summary

Transfer learning significantly improves biomedical named entity recognition (BNER) by leveraging large, noisy datasets to enhance performance on smaller, reliable datasets, reducing errors by 11%. This approach is particularly effective for smaller datasets.

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Area of Science:

Biomedical informatics
Natural Language Processing
Machine Learning

Background:

The rapid growth of biomedical literature necessitates advanced information extraction tools.
Biomedical Named Entity Recognition (BNER) is crucial for identifying key biological concepts like genes, diseases, and species.
Deep learning models, such as LSTM-CRF, show promise but rely on limited, high-quality labeled data (gold-standard corpora).

Purpose of the Study:

To investigate the effectiveness of transfer learning in improving BNER performance.
To assess the benefits of combining large, noisy silver-standard corpora (SSCs) with smaller, reliable gold-standard corpora (GSCs).

Main Methods:

Utilized a deep neural network (DNN) trained on a large, noisy SSC.
Transferred the trained DNN to a smaller, more reliable GSC for fine-tuning.
Evaluated the approach on 23 GSCs across four entity classes.

Main Results:

Transfer learning significantly improved state-of-the-art BNER results.
Achieved an average error reduction of approximately 11% compared to baseline methods.
Demonstrated particular benefit for target datasets with fewer than 6000 labeled instances.

Conclusions:

Transfer learning is a highly effective strategy for enhancing BNER performance.
Combining diverse corpora types through transfer learning addresses data limitations in biomedical NLP.
The proposed method offers a robust solution for improving information extraction from biomedical texts.