Zobrazeno 1 - 10
of 438
pro vyhledávání: '"annotation reliability"'
This paper explores a novel method for enhancing binary classification models that assess code comment quality, leveraging Generative Artificial Intelligence to elevate model performance. By integrating 1,437 newly generated code-comment pairs, label
Externí odkaz:
http://arxiv.org/abs/2410.22323
The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An observation for NLP is that emotions can be communicated implicitly by referring to events, appealing to an empatheti
Externí odkaz:
http://arxiv.org/abs/2206.05238
Autor:
Troiano, Enrica1 (AUTHOR) enrica.troiano@ims.uni-stuttgart.de, Oberländer, Laura1 (AUTHOR) laura.oberlaender@ims.uni-stuttgart.de, Klinger, Roman1 (AUTHOR) roman.klinger@ims.uni-stuttgart.de
Publikováno v:
Computational Linguistics. Mar2023, Vol. 49 Issue 1, p1-72. 72p.
Publikováno v:
Computational Linguistics, Vol 49, Iss 1 (2023)
The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An important observation for natural language processing is that emotions can be communicated implicitly by referring to
Externí odkaz:
https://doaj.org/article/f821acf1f1ad4f98af9b92d5b2bc6131
Crowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers. Unfortunately, poor worker performance frequently threatens to compromise annotation reliability, and reque
Externí odkaz:
http://arxiv.org/abs/1401.3836
Conference
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Publikováno v:
IEEE Access, Vol 8, Pp 24370-24384 (2020)
The current understanding of activity in the wireless spectrum is limited to mostly punctual studies of aggregated energy values. However, there is a need and increasing technological means for a better understanding of spectrum usage by automaticall
Externí odkaz:
https://doaj.org/article/fc93bd0bd02841128b539e81b9f66156
Autor:
Babaali, Marzieh1 (AUTHOR), Fatemi, Afsaneh1 (AUTHOR) a_fatemi@eng.ui.ac.ir, Nematbakhsh, Mohammad Ali1 (AUTHOR)
Publikováno v:
PLoS ONE. 5/23/2024, Vol. 19 Issue 5, p1-34. 34p.
Autor:
Mazumdar, Barnali1, De la Mora, Nora2, Roberts, Teresa2, Swiderski, Alexander3, Kapantzoglou, Maria2, Fergadiotis, Gerasimos2 gfergadiotis@pdx.edu
Publikováno v:
Journal of Speech, Language & Hearing Research. May2024, Vol. 67 Issue 5, p1548-1557. 10p.
Autor:
Ye, Wenjin, Ma, Yuanchen, Xiang, Junkai, Liang, Hongjie, Wang, Tao, Xiang, Qiuling, Xiang, Andy Peng, Song, Wu, Li, Weiqiang, Huang, Weijun
Reliability in cell type annotation is challenging in single-cell RNA-sequencing data analysis because both expert-driven and automated methods can be biased or constrained by their training data, especially for novel or rare cell types. Although lar
Externí odkaz:
http://arxiv.org/abs/2409.15678