Papers with annotation procedures
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision (2020.lrec-1)
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| Challenge: | Existing methods to annotate unlabeled data with emotions are expensive and time-consuming. |
| Approach: | They propose an annotation procedure that leverages Korean emotion lexicons and Korean-specific emotion features to annotate unlabeled data. |
| Outcome: | The proposed procedure compares with the KTEA dataset and a large-scale emotion-labeled dataset. |
Efficient Pairwise Annotation of Argument Quality (2020.acl-main)
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| Challenge: | Especially crowdsourcing suffers from assessors having different reference frames to base their judgments on and task instructions being nondescript and therefore unhelpful in ensuring consistency. |
| Approach: | They propose an efficient annotation framework for argument quality that uses a stochastic transitivity model and an effective sampling strategy to infer high-quality labels. |
| Outcome: | The proposed model significantly outperforms existing annotation procedures and offers statistical insights into argument quality. |
Polish Discourse Corpus (PDC): Corpus Design, ISO-Compliant Annotation, Data Highlights, and Parser Development (2024.lrec-main)
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Maciej Ogrodniczuk, Aleksandra Tomaszewska, Daniel Ziembicki, Sebastian Żurowski, Ryszard Tuora, Aleksandra Zwierzchowska
| Challenge: | The Polish Discourse Corpus employs ISO 24617-8 for discourse relation annotation. |
| Approach: | They propose to adopt ISO 24617-8 standard for discourse relation annotation for Polish and to develop a parser tailored for the framework. |
| Outcome: | The Polish Discourse Corpus adopts ISO 24617-8, a segment of the Language Resource Management – Semantic Annotation Framework (SemAF) the paper examines the corpus architecture, annotation procedures, and the challenges encountered by annotators. |
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)
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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu
| Challenge: | RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts. |
| Approach: | They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts. |
| Outcome: | The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness. |
SM-FEEL-BG - the First Bulgarian Datasets and Classifiers for Detecting Feelings, Emotions, and Sentiments of Bulgarian Social Media Text (2024.lrec-main)
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Irina Temnikova, Iva Marinova, Silvia Gargova, Ruslana Margova, Alexander Komarov, Tsvetelina Stefanova, Veneta Kireva, Dimana Vyatrova, Nevena Grigorova, Yordan Mandevski, Stefan Minkov
| Challenge: | SM-FEEL-BG is the first Bulgarian-language package for emotion detection and sentiment analysis. |
| Approach: | They introduce SM-FEEL-BG, a Bulgarian-language package that contains 6 datasets with Social Media (SM) texts with emotion, feeling, and sentiment labels and 4 classifiers trained on them. |
| Outcome: | The proposed package is the first to be released in Bulgarian and is available for free. |