@inproceedings{fischer-etal-2024-concept,
title = "Concept Over Time Analysis: Unveiling Temporal Patterns for Qualitative Data Analysis",
author = "Fischer, Tim and
Schneider, Florian and
Geislinger, Robert and
Helfer, Florian and
Koch, Gertraud and
Biemann, Chris",
editor = "Chang, Kai-Wei and
Lee, Annie and
Rajani, Nazneen",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-demo.15",
pages = "148--157",
abstract = "In this system demonstration paper, we present the Concept Over Time Analysis extension for the Discourse Analysis Tool Suite.The proposed tool empowers users to define, refine, and visualize their concepts of interest within an interactive interface. Adhering to the Human-in-the-loop paradigm, users can give feedback through sentence annotations. Utilizing few-shot sentence classification, the system employs Sentence Transformers to compute representations of sentences and concepts. Through an iterative process involving semantic similarity searches, sentence annotation, and fine-tuning with contrastive data, the model continuously refines, providing users with enhanced analysis outcomes. The final output is a timeline visualization of sentences classified to concepts. Especially suited for the Digital Humanities, Concept Over Time Analysis serves as a valuable tool for qualitative data analysis within extensive datasets. The chronological overview of concepts enables researchers to uncover patterns, trends, and shifts in discourse over time.",
}
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%0 Conference Proceedings
%T Concept Over Time Analysis: Unveiling Temporal Patterns for Qualitative Data Analysis
%A Fischer, Tim
%A Schneider, Florian
%A Geislinger, Robert
%A Helfer, Florian
%A Koch, Gertraud
%A Biemann, Chris
%Y Chang, Kai-Wei
%Y Lee, Annie
%Y Rajani, Nazneen
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F fischer-etal-2024-concept
%X In this system demonstration paper, we present the Concept Over Time Analysis extension for the Discourse Analysis Tool Suite.The proposed tool empowers users to define, refine, and visualize their concepts of interest within an interactive interface. Adhering to the Human-in-the-loop paradigm, users can give feedback through sentence annotations. Utilizing few-shot sentence classification, the system employs Sentence Transformers to compute representations of sentences and concepts. Through an iterative process involving semantic similarity searches, sentence annotation, and fine-tuning with contrastive data, the model continuously refines, providing users with enhanced analysis outcomes. The final output is a timeline visualization of sentences classified to concepts. Especially suited for the Digital Humanities, Concept Over Time Analysis serves as a valuable tool for qualitative data analysis within extensive datasets. The chronological overview of concepts enables researchers to uncover patterns, trends, and shifts in discourse over time.
%U https://aclanthology.org/2024.naacl-demo.15
%P 148-157
Markdown (Informal)
[Concept Over Time Analysis: Unveiling Temporal Patterns for Qualitative Data Analysis](https://aclanthology.org/2024.naacl-demo.15) (Fischer et al., NAACL 2024)
ACL
- Tim Fischer, Florian Schneider, Robert Geislinger, Florian Helfer, Gertraud Koch, and Chris Biemann. 2024. Concept Over Time Analysis: Unveiling Temporal Patterns for Qualitative Data Analysis. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), pages 148–157, Mexico City, Mexico. Association for Computational Linguistics.