Dataset Card for FabNER

Dataset Summary

FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition. It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process science research. For every word, there were categories/entity labels defined, namely Material (MATE), Manufacturing Process (MANP), Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR), Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format: B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.

For details about the dataset, please refer to the paper: "FabNER": information extraction from manufacturing process science domain literature using named entity recognition

Supported Tasks and Leaderboards

More Information Needed

Languages

The language in the dataset is English.

Dataset Structure

Data Instances

• Size of downloaded dataset files: 3.79 MB
• Size of the generated dataset: 6.27 MB

An example of 'train' looks as follows:

{
  "id": "0", 
  "tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."], 
  "ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}

Data Fields

fabner

• id: the instance id of this sentence, a string feature.
• tokens: the list of tokens of this sentence, a list of string features.
• ner_tags: the list of entity tags, a list of classification labels.

{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}

fabner_bio

• id: the instance id of this sentence, a string feature.
• tokens: the list of tokens of this sentence, a list of string features.
• ner_tags: the list of entity tags, a list of classification labels.

{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}

fabner_simple

• id: the instance id of this sentence, a string feature.
• tokens: the list of tokens of this sentence, a list of string features.
• ner_tags: the list of entity tags, a list of classification labels.

{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}

text2tech

• id: the instance id of this sentence, a string feature.
• tokens: the list of tokens of this sentence, a list of string features.
• ner_tags: the list of entity tags, a list of classification labels.

{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}

Data Splits

	Train	Dev	Test
fabner	9435	2183	2064

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@article{DBLP:journals/jim/KumarS22,
  author    = {Aman Kumar and
               Binil Starly},
  title     = {"FabNER": information extraction from manufacturing process science
               domain literature using named entity recognition},
  journal   = {J. Intell. Manuf.},
  volume    = {33},
  number    = {8},
  pages     = {2393--2407},
  year      = {2022},
  url       = {https://doi.org/10.1007/s10845-021-01807-x},
  doi       = {10.1007/s10845-021-01807-x},
  timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
  biburl    = {https://dblp.org/rec/journals/jim/KumarS22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @phucdev for adding this dataset.