HuSST

This is the home repository for the Hungarian version of the Stanford Sentiment Treebank. This dataset is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The corpus was created by translating and re-annotating the full sentences of the SST.

Data

The files are in the 'data' folder. The dataset contains 11 683 sentences. Each sentence is annotated for its sentiment on a three-point scale.

The train, validation and test sets contain 9 347, 1 168 and 1 168 sentences, respectively. The test set is distributed without the labels; to evaluate your model please contact us (ligeti-nagy.noemi@nytud.hu) or visit HuLU's website for an automatic evaluation (under construction). The metric of the evaluation is accuracy.

Data format

The data files are in json format. The keys are the following:

Sent_id: unique id of the instances;

Sent: the sentence;

Label: the sentiment label of the sentence: "negative", "neutral" or "positive".

Guidelines

The annotation guidelines (in Hungarian) are in the 'guidelines' folder.

License and usage

Citation

If you use this resource or any part of its documentation, please refer to:

Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. In: Berend, G., Gosztolya, G. and Vincze, V. (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. Szeged, Szegedi Tudományegyetem, Informatikai Intézet. 431–446.

@inproceedings{ligetinagy2022hulu,
  title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
  author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
  booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
  year={2022},
  pages = {431--446},
  editors = {Berend, G. and Gosztolya, G. and Vincze, V.},
  address = {Szeged},
  publisher = {Szegedi Tudományegyetem, Informatikai Intézet}
}

and

Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng and Christopher Potts (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642.

@inproceedings{socher-etal-2013-recursive,
    title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
    author = "Socher, Richard  and
      Perelygin, Alex  and
      Wu, Jean  and
      Chuang, Jason  and
      Manning, Christopher D.  and
      Ng, Andrew  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2013",
    address = "Seattle, Washington, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D13-1170",
    pages = "1631--1642",
}