Pramanick, Aniket ; Hou, Yufang ; Mohammad, Saif ; Gurevych, Iryna (2023)
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
2023 Conference on Empirical Methods in Natural Language Processing. Singapore (06.-10.12.2023)
doi: 10.18653/v1/2023.emnlp-main.142
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data. Subsequently, we leverage this structure to measure the intensity of these relationships. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics. Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Pramanick, Aniket ; Hou, Yufang ; Mohammad, Saif ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why? |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2023 |
| Ort: | Singapore |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing |
| Veranstaltungstitel: | 2023 Conference on Empirical Methods in Natural Language Processing |
| Veranstaltungsort: | Singapore |
| Veranstaltungsdatum: | 06.-10.12.2023 |
| DOI: | 10.18653/v1/2023.emnlp-main.142 |
| URL / URN: | https://aclanthology.org/2023.emnlp-main.142/ |
| Kurzbeschreibung (Abstract): | Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data. Subsequently, we leverage this structure to measure the intensity of these relationships. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics. Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact. |
| Freie Schlagworte: | UKP_p_KRITIS |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 18 Jan 2024 13:47 |
| Letzte Änderung: | 19 Mär 2024 11:36 |
| PPN: | 516393057 |
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| Suche nach Titel in: | TUfind oder in Google |
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