Proceedings ArticleDOI
A Survey of Current Datasets for Code-Switching Research
Navya Jose,Bharathi Raja Chakravarthi,Shardul Suryawanshi,Elizabeth Sherly,John P. McCrae +4 more
- pp 136-141
TLDR
A set of quality metrics to evaluate the dataset and categorize them accordingly is proposed and will assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc.Abstract:
Code switching is a prevalent phenomenon in the multilingual community and social media interaction. In the past ten years, we have witnessed an explosion of code switched data in the social media that brings together languages from low resourced languages to high resourced languages in the same text, sometimes written in a non-native script. This increases the demand for processing code-switched data to assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc. The available corpora for code switching research played a major role in advancing this area of research. In this paper, we propose a set of quality metrics to evaluate the dataset and categorize them accordingly.read more
Citations
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Proceedings ArticleDOI
Corpus creation for sentiment analysis in code-mixed Tamil-English text
TL;DR: A gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube is created and inter-annotator agreement is presented, and the results of sentiment analysis trained on this corpus are shown.
Proceedings ArticleDOI
Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text
Bharathi Raja Chakravarthi,Ruba Priyadharshini,Vigneshwaran Muralidaran,Shardul Suryawanshi,Navya Jose,Elizabeth Sherly,John P. McCrae +6 more
TL;DR: The Dravidian-CodeMix-FIRE 2020 Track as discussed by the authors focused on sentiment analysis of code-mixed text in code mixed text for Tamil and Malayalam, and participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognizing whether the comment is not in the intended language.
Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text
TL;DR: An early fusion technique is used to combine the image and text modality and compare it with a text- and an image-only baseline to investigate its effectiveness, and results show improvements in terms of Precision, Recall, and F-Score.
Proceedings ArticleDOI
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
TL;DR: In this article, a model of language models for minority and historical languages was developed using a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language
HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion
TL;DR: This paper annotated hope speech for equality, diversity and inclusion in a multilingual setting and determined that the inter-annotator agreement of their dataset using Krippendorff's alpha.
References
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