ghadeermobasher/BC5CDR-Chemical-Disease

annotations_creators:

• expert-generated language_creators:
• expert-generated languages:
• en licenses:
• unknown multilinguality:
• monolingual paperswithcode_id: bc4chemd pretty_name: BC4CHEMD size_categories:
• 1K<n<10K source_datasets:
• original task_categories:
• structure-prediction task_ids:
• named-entity-recognition

Dataset Card for BC4CHEMD

Dataset Summary

A corpus for both named entity recognition and chemical-disease relations in the literature. A total of 1500 articles have been annotated with automated assistance from PubTator. Jaccard agreement results and corpus statistics verified the reliability of the corpus.

Supported Tasks and Leaderboards

named-entity-recognition

Languages

en

Dataset Structure

Data Instances

Instances of the dataset contain an array of tokens, ner_tags and an id. An example of an instance of the dataset:

{ 'tokens': ['DPP6','as','a','candidate','gene','for','neuroleptic','-','induced','tardive','dyskinesia','.'] , 'ner_tags': [0,0,0,0,0,0,0,0,0,0,0,0], 'id': '0' }

Data Fields

• id: Sentence identifier.
• tokens: Array of tokens composing a sentence.
• ner_tags: Array of tags, where 0 indicates no disease mentioned, 1 signals the first token of a chemical and 2 the subsequent chemical tokens.

Data Splits

The data is split into a train (3500 instances), validation (3500 instances) and test set (3000 instances).

Dataset Creation

Curation Rationale

The goal of the dataset consists on improving the state-of-the-art in chemical name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.

Source Data

Initial Data Collection and Normalization

The dataset consists on abstracts extracted from PubMed.

Who are the source language producers?

The source language producers are the authors of publication abstracts hosted in PubMed.

Annotations

Annotation process

The curators were trained to mark up the text according to the labels specified in the guidelines. The raw text was not tokenized prior to the annotation and only the title was distinguished from the PubMed abstract. The selection of text spans was done at the character level, they did not allow nested annotations and distinct entity mentions should not overlap. Each text span was selected according to the annotation guidelines and classified manually into one of the CEM classes.

Who are the annotators?

The group of curators used for preparing the annotations was composed mainly of organic chemistry postgraduates with an average experience of 3-4 years in the annotation of chemical names and chemical structures.

Personal and Sensitive Information

[N/A]

Considerations for Using the Data

Social Impact of Dataset

To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets.

Discussion of Biases

The used CHEMDNER document set had to be representative and balanced in order to reflect the kind of documents that might mention the entity of interest.

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

[Needs More Information]