Advancing NLP with Cognitive Language Processing Signals

Publikation: AndetAndet bidragForskning

Standard

Advancing NLP with Cognitive Language Processing Signals. / Hollenstein, Nora.

2019.

Publikation: AndetAndet bidragForskning

Harvard

Hollenstein, N 2019, Advancing NLP with Cognitive Language Processing Signals.. <https://arxiv.org/abs/1904.02682>

APA

Hollenstein, N. (2019, apr. 4). Advancing NLP with Cognitive Language Processing Signals. https://arxiv.org/abs/1904.02682

Vancouver

Hollenstein N. Advancing NLP with Cognitive Language Processing Signals. 2019.

Author

Hollenstein, Nora. / Advancing NLP with Cognitive Language Processing Signals. 2019.

Bibtex

@misc{528a43f5cd1d4e00aa850d800cd1f3a9,
title = "Advancing NLP with Cognitive Language Processing Signals",
abstract = "When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP.",
author = "Nora Hollenstein",
year = "2019",
month = apr,
day = "4",
language = "English",
type = "Other",

}

RIS

TY - GEN

T1 - Advancing NLP with Cognitive Language Processing Signals

AU - Hollenstein, Nora

PY - 2019/4/4

Y1 - 2019/4/4

N2 - When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP.

AB - When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks. We analyze whether using such human features can show consistent improvement across tasks and data sources. We present an extensive investigation of the benefits and limitations of using cognitive processing data for NLP. Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. These methods significantly outperform the baselines and show the potential and current limitations of employing human language processing data for NLP.

M3 - Other contribution

ER -

ID: 279190296