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Description
The dataset contains 87 Scientific papers' abstracts in English randomly chosen from the folowing scientific disciplines: Biomedicine, Life Sciences, Mathematics, Medicine, Science, Humanities, Social Science.
In each text, the named entities are marked. Each name entity is linked to the corresponding Wikipedia if possible. All entities were manually verified by at least three people, which makes the dataset a high-quality gold standard for the evaluation of named entity recognition and linking algorithms.
Each marked entity in the dataset is assigned to one of the following classes:
EVENT - Named hurricanes, battles, wars, sports events, etc.
FAC - Buildings, airports, highways, bridges, etc.
GPE - Countries, cities, states
LANGUAGE - Any named language
LAW - Named documents made into laws.
LOC - Non-GPE locations, mountain ranges, bodies of water
NORP - Nationalities or religious or political groups
ORG - Companies, agencies, institutions, etc.
PERSON - People, including fictional
PRODUCT - Objects, vehicles, foods, etc. (not services)
WORK_OF_ART - Titles of books, songs, etc.
DISEASE - Names of diseases
SUBSTANCE - Natural substances
SPECIE - Species names of animals, plants, viruses, etc.
The marked entities are embedded directly in the textual files using the following syntax:
{{mention content|entity class|Wikipedia target}}
The "mention content" is a fragment of the textual file that was marked, "entity class" is the named entity class, and "Wikipedia target" is the normalized name of the English Wikipedia page describing the entity. If the entity cannot be linked sensibly to any article the target is empty but the second pipe (|) is preserved.
There is a guarantee that the double braces in the texts exist only as marked entity syntax. It allows to process the files using simple regular expression: {{[^{}]*}}
The distribution of datast NER classes, split into separate categories. The first number shows the quantity of linked entities, the second all marked mentions.
Dataset file
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.Example script for calculation:
https://github.com/antespi/s3md5
File details
- License:
-
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CC BYAttribution
Details
- Year of publication:
- 2024
- Verification date:
- 2024-05-31
- Creation date:
- 2024
- Dataset language:
- English
- Fields of science:
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- information and communication technology (Engineering and Technology)
- DOI:
- DOI ID 10.34808/pncq-fj33 open in new tab
- Series:
- Verified by:
- Gdańsk University of Technology
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