Robust Evaluation of Language–Brain Encoding Experiments

Author:

Beinborn Lisa,Abnar Samira,Choenni Rochelle

Publisher

Springer Nature Switzerland

Reference34 articles.

1. Abnar, S., Ahmed, R., Mijnheer, M., Zuidema, W.: Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity. In: Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL’18), pp. 57–66. Association for Computational Linguistics (2018). http://aclweb.org/anthology/W18-0107

2. Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) Database Theory – ICDT 2001, pp. 420–434. Springer, Berlin Heidelberg, Berlin, Heidelberg (2001). http://kops.uni-konstanz.de/bitstream/handle/123456789/5715/On_the_Surprising_Behavior_of_Distance_Metric_in_High_Dimensional_Space.pdf?sequence=1

3. Anderson, A.J., Bruni, E., Bordignon, U., Poesio, M., Baroni, M.: Of words, eyes and brains: Correlating image-based distributional semantic models with neural representations of concepts. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1960–1970. Association for Computational Linguistics (2013). http://aclweb.org/anthology/D13-1202

4. Anderson, A.J., Kiela, D., Clark, S., Poesio, M.: Visually grounded and textual semantic models differentially decode brain activity associated with concrete and abstract nouns. Trans. Assoc. Comput. Linguist. 5, 17–30 (2017). http://aclweb.org/anthology/Q17-1002

5. Artstein, R., Poesio, M.: Inter-coder agreement for computational linguistics. Comput. Linguist. 34(4), 555–596 (2008). https://www.mitpressjournals.org/doi/pdfplus/10.1162/coli.07-034-R2

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