{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chemical-detection-and-indexing-in-pubmed","title":"Chemical detection and indexing in PubMed full text articles using deep learning and rule-based methods","arxiv_id":null,"date":"2021-11-08","proceeding":"BioCreative VII Challenge Evaluation Workshop 2021 11","authors":["Tiago Almeida","Rui Antunes","João Figueira Silva","João Rafael Almeida","Sérgio Matos"],"abstract":"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.","url_abs":"https://biocreative.bioinformatics.udel.edu/media/store/files/2021/TRACK2_pos_03_BC7_submission_136.pdf","url_pdf":"https://biocreative.bioinformatics.udel.edu/media/store/files/2021/TRACK2_pos_03_BC7_submission_136.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"chemical-indexing","task_name":"Chemical Indexing"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/chemical-indexing-on-bc7-nlm-chem","task":"Chemical Indexing","dataset":"BC7 NLM-Chem","model":"Rule-based","rank_in_archive_order":2,"of":2,"metrics":{"F1-score (strict)":"0.4664"},"uses_additional_data":false},{"leaderboard":"/sota/entity-linking-on-bc7-nlm-chem","task":"Entity Linking","dataset":"BC7 NLM-Chem","model":"Sieve-based","rank_in_archive_order":2,"of":2,"metrics":{"F1-score (strict)":"0.8136"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-bc7-nlm-chem","task":"Named Entity Recognition (NER)","dataset":"BC7 NLM-Chem","model":"PubMedBERT+MLP+CRF","rank_in_archive_order":2,"of":2,"metrics":{"F1-score (strict)":"0.8454"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}