Text · dataset · 2022
chintagunta85/bc4chemd
Listed in Hugging Face Datasets
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps.
Description
When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents.
We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial.
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The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task.
In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities.
The CHEMDNER corpus and annotation guidelines are available at: biocreative.org/resources/biocreative-iv/chemdner-corpus/
Links
Where it is published
- Hugging Face dataset page huggingface.co/datasets/chintagunta85/bc4chemd ↗
landing page · from Hugging Face
Catalogue records · 1
- Hub API huggingface.co/api/datasets/chintagunta85/bc4chemd ↗
metadata API · from Hugging Face
Topics
- Stated by source
- text
- From keywords
- Computer Science & AI
- Inferred from text
- Artificial intelligence 74% · Text 75%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Hugging Face Datasets | chintagunta85/bc4chemd | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · Hugging Face | connector:huggingface@1.0.0 | /gated |
| concepts[field].anzsrc:group:4602 | enrichment · Hugging Face | taxonomy-embedding@1.1.0 | title+keywords+description (74%) |
| concepts[field].local:field:computer-science-ai | mapping · Hugging Face | connector:huggingface@1.0.0 | |
| concepts[modality].hf_modality:text | source · Hugging Face | connector:huggingface@1.0.0 | |
| concepts[modality].local:modality:text | enrichment · Hugging Face | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · Hugging Face | connector:huggingface@1.0.0 | |
| description | source · Hugging Face | connector:huggingface@1.0.0 | /description |
| publication_date | source · Hugging Face | connector:huggingface@1.0.0 | |
| title | source · Hugging Face | connector:huggingface@1.0.0 | /id |
| updated_date | source · Hugging Face | connector:huggingface@1.0.0 |