--- annotations_creators: - unknown language_creators: - unknown language: - en license: - unknown multilinguality: - monolingual size_categories: - 100KPresent-Reordered-Mixed-Unseen) scheme as proposed in the following paper: - Florian Boudin and Ygor Gallina. 2021. [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics. Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text. ## Content The dataset is divided into the following three splits: | Split | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen | | :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: | | Train | 530 809 | 5.29 | 58.19 | 10.93 | 17.36 | 13.52 | | Test | 20 000 | 5.28 | 58.40 | 10.84 | 17.20 | 13.56 | | Validation | 20 000 | 5.27 | 58.20 | 10.94 | 17.26 | 13.61 | The following data fields are available: - **id**: unique identifier of the document. **NB** There were no ids in the original dataset. The ids were generated using the python module shortuuid (https://pypi.org/project/shortuuid/) - **title**: title of the document. - **abstract**: abstract of the document. - **keyphrases**: list of the author assigned keyphrases. - **prmu**: list of Present-Reordered-Mixed-Unseen categories for reference keyphrases. **NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + abstract).