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Chemical detection and indexing in PubMed full text articles using deep learning and rule-based methods

8 Nov 2021BioCreative VII Challenge Evaluation Workshop 2021 11archive 2025-07-28

Tiago Almeida, Rui Antunes, João Figueira Silva, João Rafael Almeida, Sérgio Matos

Identifying chemicals in biomedical scientific literature is a crucial task for drug development research. The BioCreative NLM-Chem challenge promoted the development of automatic systems that can identify chemicals in full-text articles and decide which chemical concepts are relevant to be indexed. This work describes the participation of the BIT.UA team from the University of Aveiro, where we propose a three-stage automatic pipeline that individually tackles (i) chemical mention detection, (ii) entity normalization and (iii) indexing. We adopted a deep learning solution based on a biomedical BERT variant for chemical identification. For normalization we used a rule-based approach and a hybrid version that explores a dense retrieval mechanism. Similarly, for indexing we also followed two distinct approaches: a rule-based, and a TF-IDF based method. Our best official results are consistently above the official median and benchmark in the three subtasks, with respectively 0.8454, 0.8136, and 0.4664 F1-scores.

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Tasks

Articles Chemical Indexing Entity Linking Named Entity Recognition (NER)Retrieval

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Chemical Indexing	BC7 NLM-Chem	Rule-based	F1-score (strict)	0.4664	#2 of 2	Archive leaderboard	report
Entity Linking	BC7 NLM-Chem	Sieve-based	F1-score (strict)	0.8136	#2 of 2	Archive leaderboard	report
Named Entity Recognition (NER)	BC7 NLM-Chem	PubMedBERT+MLP+CRF	F1-score (strict)	0.8454	#2 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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