Automated Text Annotation System

Context:
- It can range from being a Fully-Automated Text Annotation System to being a Partially-Automated Text Annotation System, depending on the level of human intervention required.
- It can include features like Automatic Labeling, Ranking Scores.
- It can (often) approximate a Text Prediction System, though possibly support lower latency and cost requirements.
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Example(s):
- SDOI Annotation System, known for its specific application in domain-oriented investigations.
- Marky Annotation System, which supports annotation consistency in multi-user and iterative document annotation projects.
- FITAnnotator, a flexible and intelligent system introduced in 2021 for effective text annotation.
- ...
Counter-Example(s):
- a Manual Annotation System, which relies entirely on human effort for annotation tasks.
- a Automated Image Annotation System, which annotates images rather than text documents.
See: Automated Classification System, Natural Language Processing, Machine Learning.

References

(Li et al., 2021) ⇒ Y. Li, B. Yu, L. Quangang, and T. Liu. (2021). “FITAnnotator: A Flexible and Intelligent Text Annotation System.” In: Proceedings of the 2021 Conference of the …. [1]
- QUOTE: “...With such a model, we can obtain automatic labeling results for unlabeled data, and calculate their ranking scores according to the active learning strategy...”
- NOTE: It introduces FITAnnotator, a text annotation system that utilizes an active learning strategy to automatically label and rank unlabeled data, demonstrating flexibility and intelligence in text annotation.

(Fraga et al., 2021) ⇒ T. Fraga, A. Barros, and O. Belo. (2021). “An Annotation System for Linguistic, Geographic and Sociocultural Research in an Academic Context.” In: EDULEARN21 Proceedings. [2]
- QUOTE: “...An automatic annotation system is built using natural language processing mechanisms...tasks that can be found in an automatic text annotation system. Usually, the text annotation...”
- NOTE: It discusses the creation of an automatic annotation system leveraging natural language processing for research in linguistic, geographic, and sociocultural fields within an academic setting.

(Tsai et al., 2018) ⇒ H.T. Tsai, C.M. Chen, and C.Y. Liu. (2018). “An Automatic Text Annotation System to Improve Reading Comprehension of Chinese ancient texts.” In: 2018 7th International Congress. [3]
- QUOTE: “...MARKUS semi-automatic text annotation system...without the function of automatic word segmentation...study developed an automatic text annotation system...”
- NOTE: It describes the development of an automatic text annotation system for enhancing the reading comprehension of Chinese ancient texts, highlighting the limitations of existing systems like MARKUS in automatic word segmentation.

(Chen et al., 2018) ⇒ C.M. Chen, Y.T. Chen, and C.Y. Liu. (2018). “Development and Evaluation of an Automatic Text Annotation System for Supporting Digital Humanities Research.” In: Library Hi Tech. [4]
- QUOTE: “...MARKUS semi-automatic text annotation system is...absence of the function of automatic word segmentation...an automatic text annotation system for...”
- NOTE: It focuses on the development and evaluation of an automatic text annotation system named ATAS, designed to aid digital humanities research, addressing issues like the lack of automatic word segmentation in previous systems.

(Pérez-Pérez et al., 2014) ⇒ Martín Pérez-Pérez, Daniel Glez-Peña, Florentino Fdez-Riverola, and Anália Lourenço. (2014). “Marky: A Tool Supporting Annotation Consistency in Multi-user and Iterative Document Annotation Projects.” In: Computer Methods and Programs in Biomedicine. doi:10.1016/j.cmpb.2014.11.005