Challenging distributional models with a conceptual network of philosophical terms
| Autor: | Oortwijn, Y., Bloem, J., Sommerauer, P., Meyer, F., Zhou, W., Fokkens, A., Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. |
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| Přispěvatelé: | ILLC (FGw), Logic and Language (ILLC, FNWI/FGw) |
| Rok vydání: | 2021 |
| Předmět: |
Ground truth
Small data Computer science 06 humanities and the arts 02 engineering and technology 0603 philosophy ethics and religion Data science Conceptual network Blueprint 060302 philosophy Close reading 0202 electrical engineering electronic engineering information engineering Literary criticism 020201 artificial intelligence & image processing |
| Zdroj: | NAACL-HLT The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021, 2511-2522 STARTPAGE=2511;ENDPAGE=2522;TITLE=The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
| DOI: | 10.18653/v1/2021.naacl-main.199 |
| Popis: | Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on methods for small data is still in early stages and it is not clear which methods achieve the best results. We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. We compare three methods for creating specialized models from small datasets. Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small data yield promising directions for future work. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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