metadata
license: cc-by-4.0
language:
- hi
- en
tags:
- code-mixing
- Hinglish
- expert-annotated
size_categories:
- 1M<n<10M
configs:
- config_name: LID
data_files:
- split: train
path: LID_train.csv
- split: test
path: LID_test.csv
- config_name: POS
data_files:
- split: train
path: POS_train.csv
- split: test
path: POS_test.csv
- config_name: NER
data_files:
- split: train
path: NER_train.csv
- split: test
path: NER_test.csv
- config_name: MLI
data_files:
- split: train
path: MLI_train.csv
- split: test
path: MLI_test.csv
- config_name: Translation
data_files:
- split: train
path: Translation_train.csv
- split: test
path: Translation_test.csv
Dataset Details
COMI-LINGUA (COde-MIxing and LINGuistic Insights on Natural Hinglish Usage and Annotation) is a high-quality Hindi-English code-mixed dataset, manually annotated by three annotators. It serves as a benchmark for multilingual NLP models by covering multiple foundational tasks.
COMI-LINGUA provides annotations for several key NLP tasks:
- Language Identification (LID): Token-wise classification of Hindi, English, and other linguistic units.
- Matrix Language Identification (MLI): Sentence-level annotation of the dominant language.
- Part-of-Speech (POS) Tagging: Syntactic categorization for linguistic analysis.
- Named Entity Recognition (NER): Identification of entities in Hinglish text.
- Translation: Parallel translation of sentences in Romanized Hindi and Devanagari Hindi and English languages.
Dataset Description
- Curated by: Lingo Research Group at IIT Gandhinagar, India
- Funded by: SERB
- Language(s) (NLP): Bilingual (Hindi [hi], English [en])
- License: cc-by-4.0