Advancing NLP with Cognitive Language Processing Signals
Publikation: Andet › Andet bidrag › Forskning
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Advancing NLP with Cognitive Language Processing Signals. / Hollenstein, Nora.
2019.Publikation: Andet › Andet bidrag › Forskning
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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