Please send an email to Thomas Kober (t.kober@rasa.com) if you need access to the DIASPORA dataset introduced in the paper Aspectuality across Genre: A Distributional Semantics Approach
Due to licensing issues, we haven't figured out a better way for releasing the data yet - sorry :/.
The downsampled SitEnt dataset (train and test split) can be found in the datasets folder.
Please use the following citation if you use the DIASPORA or our downsampled SitEnt-ambig dataset in any of your work:
@inproceedings{kober-etal-2020-aspectuality,
title = "Aspectuality Across Genre: A Distributional Semantics Approach",
author = "Kober, Thomas and
Alikhani, Malihe and
Stone, Matthew and
Steedman, Mark",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.401",
doi = "10.18653/v1/2020.coling-main.401",
pages = "4546--4562",
abstract = "The interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb{'}s local context is most indicative of its aspectual class, and we demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Further, we present a new dataset of human-human conversations annotated with lexical aspects and present experiments that show the correlation of telicity with genre and discourse goals.",
}