community-datasets/offenseval_dravidian
收藏数据集卡片 - Offenseval Dravidian
数据集描述
数据集摘要
Offensive language identification 是自然语言处理（NLP）中的分类任务，旨在对社交媒体中的冒犯性内容进行审核和最小化。该任务在过去二十年中一直是学术界和工业界的活跃研究领域。随着社交媒体上大量代码混合文本的冒犯性识别需求增加，代码混合现象在多语言社区中普遍存在，且代码混合文本有时使用非本地脚本编写。基于单语数据的系统在代码混合数据上表现不佳，因为文本中代码切换的复杂性在不同语言层次上存在。本共享任务为 Dravidian 语言（Tamil-English、Malayalam-English 和 Kannada-English）中的代码混合文本提供了新的黄金标准语料库，用于冒犯性语言识别。
支持的任务和排行榜
该任务的目标是识别来自社交媒体收集的 Dravidian 语言（Tamil-English、Malayalam-English 和 Kannada-English）代码混合数据集的评论/帖子的冒犯性内容。评论/帖子可能包含多个句子，但语料库的平均句子长度为 1。每个评论/帖子在评论/帖子级别进行标注。该数据集还存在类别不平衡问题，反映了现实世界的情况。
语言
Dravidian 语言中的代码混合文本（Tamil-English、Malayalam-English 和 Kannada-English）。
数据集结构
数据实例
Tamil 数据集示例
| text | label |
|---|---|
| படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level | Not_offensive |
| Avasara patutiya editor uhh antha bullet sequence aa nee soliruka kudathu, athu sollama iruntha movie ku konjam support aa surprise element aa irunthurukum | Not_offensive |
Malayalam 数据集示例
| text | label |
|---|---|
| ഷൈലോക്ക് ന്റെ നല്ല ടീസർ ആയിട്ട് പോലും ട്രോളി നടന്ന ലാലേട്ടൻ ഫാൻസിന് കിട്டിയൊരു നല്ലൊരു തിരിച്ചടി തന്നെ ആയിരിന്നു ബിഗ് ബ്രദർ ന്റെ ട്രെയ്ലർ | Not_offensive |
| Marana mass Ekka kku kodukku oru | Not_offensive |
Kannada 数据集示例
| text | label |
|---|---|
| ನಿಜವಾಗಿಯೂ ಅದ್ಭುತ heartly heltidini... plz avrigella namma nimmellara supprt beku | Not_offensive |
| Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. | Not_offensive |
数据字段
Tamil
text: Tamil-English 代码混合评论。label: 从 0 到 5 的整数，对应以下值："Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Tamil"
Malayalam
text: Malayalam-English 代码混合评论。label: 从 0 到 5 的整数，对应以下值："Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-malayalam"
Kannada
text: Kannada-English 代码混合评论。label: 从 0 到 5 的整数，对应以下值："Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Kannada"
数据分割
| train | validation | |
|---|---|---|
| Tamil | 35139 | 4388 |
| Malayalam | 16010 | 1999 |
| Kannada | 6217 | 777 |
数据集创建
策划理由
社交媒体文本中对冒犯性语言识别的需求日益增加，这些文本大多是代码混合的。代码混合现象在多语言社区中普遍存在，且代码混合文本有时使用非本地脚本编写。基于单语数据的系统在代码混合数据上表现不佳，因为文本中代码切换的复杂性在不同语言层次上存在。
源数据
初始数据收集和规范化
[需要更多信息]
源语言生产者是谁？
YouTube 用户
标注
标注过程
[需要更多信息]
标注者是谁？
[需要更多信息]
个人和敏感信息
[需要更多信息]
使用数据集的注意事项
数据集的社会影响
[需要更多信息]
偏见的讨论
[需要更多信息]
其他已知限制
[需要更多信息]
附加信息
数据集策展人
[需要更多信息]
许可信息
本作品根据Creative Commons Attribution 4.0 International Licence进行许可。
引用信息
@article{chakravarthi-etal-2021-lre, title = "DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text", author = "Chakravarthi, Bharathi Raja and Priyadharshini, Ruba and Muralidaran, Vigneshwaran and Jose, Navya and Suryawanshi, Shardul and Sherly, Elizabeth and McCrae, John P", journal={Language Resources and Evaluation}, publisher={Springer} }
@inproceedings{dravidianoffensive-eacl, title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada}, author={Chakravarthi, Bharathi Raja and Priyadharshini, Ruba and Jose, Navya and M, Anand Kumar and Mandl, Thomas and Kumaresan, Prasanna Kumar and Ponnsamy, Rahul and V,Hariharan and Sherly, Elizabeth and McCrae, John Philip }, booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages", month = April, year = "2021", publisher = "Association for Computational Linguistics", year={2021} }
@inproceedings{hande-etal-2020-kancmd, title = "{K}an{CMD}: {K}annada {C}ode{M}ixed Dataset for Sentiment Analysis and Offensive Language Detection", author = "Hande, Adeep and Priyadharshini, Ruba and Chakravarthi, Bharathi Raja", booktitle = "Proceedings of the Third Workshop on Computational Modeling of Peoples Opinions, Personality, and Emotions in Social Media", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.peoples-1.6", pages = "54--63", abstract = "We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.", }
@inproceedings{chakravarthi-etal-2020-corpus, title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text", author = "Chakravarthi, Bharathi Raja and Muralidaran, Vigneshwaran and Priyadharshini, Ruba and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.28", pages = "202--210", abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.", language = "English", ISBN = "979-10-95546-35-1", }
@inproceedings{chakravarthi-etal-2020-sentiment, title = "A Sentiment Analysis Dataset for Code-Mixed {M}alayalam-{E}nglish", author = "Chakravarthi, Bharathi Raja and Jose, Navya and Suryawanshi, Shardul and Sherly, Elizabeth and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.25", pages = "177--184", abstract = "There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff{}s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.", language = "English", ISBN = "979-10-95546-35-1", }
贡献
感谢 @jamespaultg 添加此数据集。
