ArticlePloS one2014
Annotated chemical patent corpus: a gold standard for text mining.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- AI-assisted interpretation of Markush structures in pharmaceutical patents: a review of emerging tools, datasets, and challenges.Journal of cheminformatics · 2026Review
- Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.JMIR AI · 2024Article
- OSPAR: A Corpus for Extraction of Organic Synthesis Procedures with Argument Roles.Journal of chemical information and modeling · 2023Article
- Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature.Journal of biomedical informatics · 2023Article
- Article
- Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.Briefings in bioinformatics · 2022Article
- ChemTables: a dataset for semantic classification on tables in chemical patents.Journal of cheminformatics · 2021Article
- ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents.Frontiers in research metrics and analytics · 2021Article
- From Tokenization to Self-Supervision: Building a High-Performance Information Extraction System for Chemical Reactions in Patents.Frontiers in research metrics and analytics · 2021Article
- Ensemble of Deep Masked Language Models for Effective Named Entity Recognition in Health and Life Science Corpora.Frontiers in research metrics and analytics · 2021Article
- Automatic identification of relevant chemical compounds from patents.Database : the journal of biological databases and curation · 2019Article
- Transfer learning for biomedical named entity recognition with neural networks.Bioinformatics (Oxford, England) · 2018Article
- Deep learning with word embeddings improves biomedical named entity recognition.Bioinformatics (Oxford, England) · 2017Article
- NERChem: adapting NERBio to chemical patents via full-token features and named entity feature with chemical sub-class composition.Database : the journal of biological databases and curation · 2016Article
- SureChEMBL: a large-scale, chemically annotated patent document database.Nucleic acids research · 2016Article
- Recognizing chemicals in patents: a comparative analysis.Journal of cheminformatics · 2016Article
- Chemical entity recognition in patents by combining dictionary-based and statistical approaches.Database : the journal of biological databases and curation · 2016Article
- Patterns of database citation in articles and patents indicate long-term scientific and industry value of biological data resources.F1000Research · 2016Article
- Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning.Database : the journal of biological databases and curation · 2016Article
- Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.Journal of cheminformatics · 2015Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.