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1000
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Abstract/Summary
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Named Entity Recognition (NER) is crucial for various Natural Language Processing (NLP) tasks, including uncovering insights from vast textual datasets. We evaluated Bidirectional Encoder Representations from Transformers (BERT) models pre-trained on general data, fine-tuning them on the COPIOUS dataset for biodiversity NER. Achieving the most optimal performance, our DeBERTa NER model was employed in a biodiversity Information Extraction pipeline, which was applied on the forestry compendium of the Centre for Agricultural and Biosciences International Digital Library. We demonstrate that the pipeline enables the enrichment of descriptive information on reproductive conditions and habitats of tree species.
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