The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension
Shohini Bhattasali, Jonathan Brennan, Wen-Ming Luh, Berta Franzluebbers, John Hale
Abstract
The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.- Anthology ID:
- 2020.lrec-1.15
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 120–125
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.15
- DOI:
- Bibkey:
- Cite (ACL):
- Shohini Bhattasali, Jonathan Brennan, Wen-Ming Luh, Berta Franzluebbers, and John Hale. 2020. The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 120–125, Marseille, France. European Language Resources Association.
- Cite (Informal):
- The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension (Bhattasali et al., LREC 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.lrec-1.15.pdf
Export citation
@inproceedings{bhattasali-etal-2020-alice,
title = "The Alice Datasets: f{MRI} {\&} {EEG} Observations of Natural Language Comprehension",
author = "Bhattasali, Shohini and
Brennan, Jonathan and
Luh, Wen-Ming and
Franzluebbers, Berta and
Hale, John",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.15",
pages = "120--125",
abstract = "The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings %T The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension %A Bhattasali, Shohini %A Brennan, Jonathan %A Luh, Wen-Ming %A Franzluebbers, Berta %A Hale, John %Y Calzolari, Nicoletta %Y Béchet, Frédéric %Y Blache, Philippe %Y Choukri, Khalid %Y Cieri, Christopher %Y Declerck, Thierry %Y Goggi, Sara %Y Isahara, Hitoshi %Y Maegaard, Bente %Y Mariani, Joseph %Y Mazo, Hélène %Y Moreno, Asuncion %Y Odijk, Jan %Y Piperidis, Stelios %S Proceedings of the Twelfth Language Resources and Evaluation Conference %D 2020 %8 May %I European Language Resources Association %C Marseille, France %@ 979-10-95546-34-4 %G English %F bhattasali-etal-2020-alice %X The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain. %U https://aclanthology.org/2020.lrec-1.15 %P 120-125
Markdown (Informal)
[The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension](https://aclanthology.org/2020.lrec-1.15) (Bhattasali et al., LREC 2020)
- The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension (Bhattasali et al., LREC 2020)
ACL
- Shohini Bhattasali, Jonathan Brennan, Wen-Ming Luh, Berta Franzluebbers, and John Hale. 2020. The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 120–125, Marseille, France. European Language Resources Association.