The rise of social media has facilitated easier communication, information sharing, and current affairs updates. However, the prevalence of misleading and deceptive content, commonly referred to as fake news, poses a significant challenge. This paper focuses on the classification of fake news in Malayalam, a Dravidian language, utilizing natural language processing (NLP) techniques. To develop a model, we employed a random forest machine learning method on a dataset provided by a shared task(DravidianLangTech@EACL 2024)1. When evaluated by the separate test dataset, our developed model achieved a 0.71 macro F1 measure.

This research tackles the issue of fake news by utilizing the RNN-LSTM deep learning method with optimized hyperparameters identified through grid search. The model’s performance in multi-label classification is hindered by unbalanced data, despite its success in binary classification. We achieved a score of 0.82 in the binary classification task, whereas in the multi-class task, the score was 0.32. We suggest incorporating data balancing techniques for researchers who aim to further this task, aiming to improve results in managing a variety of information.

Even though the improper use of social media is increasing nowadays, there is also technology that brings solutions. Here, improperness is posting hate and offensive speech that might harm an individual or group. Hate speech refers to an insult toward an individual or group based on their identities. Spreading it on social media platforms is a serious problem for society. The solution, on the other hand, is the availability of natural language processing(NLP) technology that is capable to detect and handle such problems. This paper presents the detection of social media’s hate and offensive speech in the code-mixed Telugu language. For this, the task and golden standard dataset were provided for us by the shared task organizer (DravidianLangTech@ EACL 2024)1. To this end, we have employed the TF-IDF technique for numeric feature extraction and used a random forest algorithm for modeling hate speech detection. Finally, the developed model was evaluated on the test dataset and achieved 0.492 macro-F1.

With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively

This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.

This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.

In this paper, we present a parallel Spanish- Mazatec and Spanish-Mixtec corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two indigenous Mexican languages. We evaluated the usability of the collected corpus using three different approaches: transformer, transfer learning, and fine-tuning pre-trained multilingual MT models. Fine-tuning the Facebook m2m100-48 model outperformed the other approaches, with BLEU scores of 12.09 and 22.25 for Mazatec-Spanish and Spanish-Mazatec translations, respectively, and 16.75 and 22.15 for Mixtec-Spanish and Spanish-Mixtec translations, respectively. The results indicate that translation performance is influenced by the dataset size (9,799 sentences in Mazatec and 13,235 sentences in Mixtec) and is more effective when indigenous languages are used as target languages. The findings emphasize the importance of creating parallel corpora for indigenous languages and fine-tuning models for low-resource translation tasks. Future research will investigate zero-shot and few-shot learning approaches to further improve translation performance in low-resource settings.

This paper describes CIC NLP’s submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas. We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) — Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups. We experimented with 11 languages from America and report the setups we used as well as the results we achieved. Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages.

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia.Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This repository can be updated periodically with contributions from other researchers. Our objective is to disseminate information to NLP researchers interested in Ethiopian languages and encourage future research in this domain.

Language Identification at the Word Level in Kannada-English Texts. This paper describes the system paper of CoLI-Kanglish 2022 shared task. The goal of this task is to identify the different languages used in CoLI-Kanglish 2022. This dataset is distributed into different categories including Kannada, English, Mixed-Language, Location, Name, and Others. This Code-Mix was compiled by CoLI-Kanglish 2022 organizers from posts on social media. We use two classification techniques, KNN and SVM, and achieve an F1-score of 0.58 and place third out of nine competitors.

The goal of code-mixed language identification (LID) is to determine which language is spoken or written in a given segment of a speech, word, sentence, or document. Our task is to identify English, Kannada, and mixed language from the provided data. To train a model we used the CoLI-Kenglish dataset, which contains English, Kannada, and mixed-language words. In our work, we conducted several experiments in order to obtain the best performing model. Then, we implemented the best model by using Bidirectional Long Short Term Memory (Bi-LSTM), which outperformed the other trained models with an F1-score of 0.61%.

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.