Papers with text annotation
FITAnnotator: A Flexible and Intelligent Text Annotation System (2021.naacl-demos)
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| Challenge: | In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation. |
| Approach: | They propose a generic web-based tool for efficient text annotation. |
| Outcome: | The proposed tool is based on a fully modular architecture and provides three kinds of interfaces to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers. |
NLATool: an Application for Enhanced Deep Text Understanding (C18-2)
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Markus Gärtner, Sven Mayer, Valentin Schwind, Eric Hämmerle, Emine Turcan, Florin Rheinwald, Gustav Murawski, Lars Lischke, Jonas Kuhn
| Challenge: | a wide range of subfields in natural language processing see systems solving their tasks with sufficiently high-quality levels. |
| Approach: | They propose a web application that supports text annotation and enriches the text with additional information from a number of sources directly within the application. |
| Outcome: | The proposed web application is based on a human-centered design process . it offers a rich visualization of texts and the entities mentioned in them through an easy to use interface. |
ITAKE: Interactive Unstructured Text Annotation and Knowledge Extraction System with LLMs and ModelOps (2024.acl-demos)
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| Challenge: | Unstructured text data contains a large amount of valuable knowledge, but there are many tools that do not meet the needs of actual business. |
| Approach: | They propose an unstructured text annotation and knowledge extraction system that integrates Large Language Models and ModelOps to improve model supervision and performance. |
| Outcome: | The proposed system integrates large language models and ModelOps to improve performance in low-resource contexts. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
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| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
An Annotation Language for Semantic Search of Legal Sources (L18-1)
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| Challenge: | formalizing legal sources is an important challenge, but the generation of a formal representation from legal texts has been less considered and requires considerable expertise. |
| Approach: | They propose to experiment with annotations and the annotation process to improve uniformity and efficiency of legal annotation. |
| Outcome: | The proposed method improves the richness and efficiency of legal annotations. |
Which Demographics do LLMs Default to During Annotation? (2025.acl-long)
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Johannes Schäfer, Aidan Combs, Christopher Bagdon, Jiahui Li, Nadine Probol, Lynn Greschner, Sean Papay, Yarik Menchaca Resendiz, Aswathy Velutharambath, Amelie Wuehrl, Sabine Weber, Roman Klinger
| Challenge: | Demographics and cultural background of annotators influence the labels they assign in text annotation. |
| Approach: | They examine the attributes of human annotators LLMs inherently mimic and compare them to demographic-conditioned prompts and placebo-conditioned ones. |
| Outcome: | The proposed model incorporates demographics and cultural background into the output of the large language models (LLMs) to evaluate which attributes of human annotators LLMs inherently mimic. |