Datasets:
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- kn
- ml
- ta
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Offenseval Dravidian
config_names:
- kannada
- malayalam
- tamil
tags:
- offensive-language
dataset_info:
- config_name: kannada
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not_offensive
'1': Offensive_Untargetede
'2': Offensive_Targeted_Insult_Individual
'3': Offensive_Targeted_Insult_Group
'4': Offensive_Targeted_Insult_Other
'5': not-Kannada
splits:
- name: train
num_bytes: 567119
num_examples: 6217
- name: validation
num_bytes: 70147
num_examples: 777
download_size: 678727
dataset_size: 637266
- config_name: malayalam
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not_offensive
'1': Offensive_Untargetede
'2': Offensive_Targeted_Insult_Individual
'3': Offensive_Targeted_Insult_Group
'4': Offensive_Targeted_Insult_Other
'5': not-malayalam
splits:
- name: train
num_bytes: 1944849
num_examples: 16010
- name: validation
num_bytes: 249360
num_examples: 1999
download_size: 1207152
dataset_size: 2194209
- config_name: tamil
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not_offensive
'1': Offensive_Untargetede
'2': Offensive_Targeted_Insult_Individual
'3': Offensive_Targeted_Insult_Group
'4': Offensive_Targeted_Insult_Other
'5': not-Tamil
splits:
- name: train
num_bytes: 4214785
num_examples: 35139
- name: validation
num_bytes: 526104
num_examples: 4388
download_size: 2690062
dataset_size: 4740889
configs:
- config_name: malayalam
data_files:
- split: train
path: malayalam/train-*
- split: validation
path: malayalam/validation-*
- config_name: tamil
data_files:
- split: train
path: tamil/train-*
- split: validation
path: tamil/validation-*
Dataset Card for Offenseval Dravidian
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://competitions.codalab.org/competitions/27654#learn_the_details
- Repository: https://competitions.codalab.org/competitions/27654#participate-get_data
- Paper: Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada
- Leaderboard: https://competitions.codalab.org/competitions/27654#results
- Point of Contact: Bharathi Raja Chakravarthi
Dataset Summary
Offensive language identification is classification task in natural language processing (NLP) where the aim is to moderate and minimise offensive content in social media. It has been an active area of research in both academia and industry for the past two decades. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for offensive language identification of code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).
Supported Tasks and Leaderboards
The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
Languages
Code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).
Dataset Structure
Data Instances
An example from the Tamil dataset looks as follows:
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 |
An example from the Malayalam dataset looks as follows:
text | label |
---|---|
ഷൈലോക്ക് ന്റെ നല്ല ടീസർ ആയിട്ട് പോലും ട്രോളി നടന്ന ലാലേട്ടൻ ഫാൻസിന് കിട്ടിയൊരു നല്ലൊരു തിരിച്ചടി തന്നെ ആയിരിന്നു ബിഗ് ബ്രദർ ന്റെ ട്രെയ്ലർ | Not_offensive |
Marana mass Ekka kku kodukku oru | Not_offensive |
An example from the Kannada dataset looks as follows:
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 |
Data Fields
Tamil
text
: Tamil-English code mixed comment.label
: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Tamil"
Malayalam
text
: Malayalam-English code mixed comment.label
: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-malayalam"
Kannada
text
: Kannada-English code mixed comment.label
: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Kannada"
Data Splits
train | validation | |
---|---|---|
Tamil | 35139 | 4388 |
Malayalam | 16010 | 1999 |
Kannada | 6217 | 777 |
Dataset Creation
Curation Rationale
There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text.
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
Youtube users
Annotations
Annotation process
[Needs More Information]
Who are the annotators?
[Needs More Information]
Personal and Sensitive Information
[Needs More Information]
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
[Needs More Information]
Licensing Information
This work is licensed under a Creative Commons Attribution 4.0 International Licence
Citation Information
@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 People's Opinions, Personality, and Emotion's 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",
}
Contributions
Thanks to @jamespaultg for adding this dataset.