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--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- ko |
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language_creators: |
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- expert-generated |
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license: cc-by-sa-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: KorFin-ABSA |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- klue |
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tags: |
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- sentiment analysis |
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- aspect based sentiment analysis |
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- finance |
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task_categories: |
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- text-classification |
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task_ids: |
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- topic-classification |
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- sentiment-classification |
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--- |
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|
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# Dataset Card for KorFin-ABSA |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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### Dataset Summary |
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The KorFin-ASC is an extension of KorFin-ABSA including 8818 samples with (aspect, polarity) pairs annotated. |
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The samples were collected from [KLUE-TC](https://klue-benchmark.com/tasks/66/overview/description) and |
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analyst reports from [Naver Finance](https://finance.naver.com). |
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Annotation of the dataset is described in the paper [Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance](https://arxiv.org/abs/2301.03136). |
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### Supported Tasks and Leaderboards |
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This dataset supports the following tasks: |
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* Aspect-Based Sentiment Classification |
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### Languages |
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Korean |
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## Dataset Structure |
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### Data Instances |
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Each instance consists of a single sentence, aspect, and corresponding polarity (POSITIVE/NEGATIVE/NEUTRAL). |
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``` |
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{ |
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"title": "LGU＋ 1분기 영업익 1천706억원…마케팅 비용 감소", |
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"aspect": "LG U+", |
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'sentiment': 'NEUTRAL', |
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'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008363739', |
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'annotator_id': 'A_01', |
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'Type': 'single' |
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} |
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``` |
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### Data Fields |
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* title: |
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* aspect: |
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* sentiment: |
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* url: |
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* annotator_id: |
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* url: |
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### Data Splits |
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The dataset currently does not contain standard data splits. |
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## Additional Information |
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You can download the data via: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("amphora/KorFin-ASC") |
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``` |
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Please find more information about the code and how the data was collected in the paper [Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance](https://arxiv.org/abs/2301.03136). |
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The best-performing model on this dataset can be found at [link](https://huggingface.co/amphora/KorFinASC-XLM-RoBERTa). |
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### Licensing Information |
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KorFin-ASC is licensed under the terms of the [cc-by-sa-4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
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### Citation Information |
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Please cite this data using: |
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``` |
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@article{son2023removing, |
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title={Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance}, |
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author={Son, Guijin and Lee, Hanwool and Kang, Nahyeon and Hahm, Moonjeong}, |
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journal={arXiv preprint arXiv:2301.03136}, |
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year={2023} |
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} |
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``` |
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### Contributions |
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Thanks to [@Albertmade](https://github.com/h-albert-lee), [@amphora](https://github.com/guijinSON) for making this dataset. |
