Russian Scientific Entity Recognition and Relation Classification (RuSERRC) dataset

Contains abstracts of 1,680 scientific papers on information technology in Russian:

• 1,600 unlabeled
• 80 manually labeled.

The dataset is available as a release.

⭐️ Cross-domain RuSERRC

See details here.

Entity recognition

Entity annotation was performed in BIO format. Entities are terms represented by nouns or noun phrases.

Relation Classification

The following types were used to annotate semantic relations:

• CAUSE
• COMPARE
• ISA
• PART_OF
• SYNONYMS
• TOOL
• USAGE

Nested entities

See details here.

Entity linking

See details here.

Aspects

See details here.

Citation

If you find this repository useful, feel free to cite our paper: Marshalova A., Bruches E., Batura T. Automatic Aspect Extraction from Scientific Texts. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2023. Communications in Computer and Information Science. Springer, 2024. V. 1905. pp. 67–80.

@inproceedings{ruserrc-aspect,
  title={Automatic Aspect Extraction from Scientific Texts},
  author={Marshalova, Anna and Bruches, Elena and Batura, Tatiana},
  booktitle={Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2023. Communications in Computer and Information Science},
  publisher={Springer Nature Switzerland},  
  year={2024},
  pages={67--80}
}

Bruches E., Pauls A., Batura T., Isachenko V. Entity recognition and relation extraction from scientific and technical texts in Russian. Proceedings of 2020 Science and Artificial Intelligence conference (S.A.I.ence). IEEE. 2020. p. 41-45.

@inproceedings{ruserrc-dataset,
  title={Entity recognition and relation extraction from scientific and technical texts in Russian},
  author={Bruches, Elena and Pauls, Alexey and Batura, Tatiana and Isachenko, Vladimir},
  booktitle={2020 Science and Artificial Intelligence conference (SAI ence)},
  pages={41--45},
  year={2020}
}