Rajalakshmi Sivanaiah


2022

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SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model
Praveenkumar Vijayakumar | Prathyush S | Aravind P | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.

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SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models
Sarika Esackimuthu | Shruthi Hariprasad | Rajalakshmi Sivanaiah | Angel S | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.

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SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text
Karun Anantharaman | Angel S | Rajalakshmi Sivanaiah | Saritha Madhavan | Sakaya Milton Rajendram
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely “not depressed”,“moderately depressed”, and “severely de-pressed”. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN_MLRG1 achieves 58.5%accuracy on the DepSign-LT-EDI@ACL-2022test set.

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TechSSN at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models
Rajalakshmi Sivanaiah | Angel S | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Research is progressing in a fast manner in the field of offensive, hate speech, abusive and sarcastic data. Tackling hate speech against women is urgent and really needed to give respect to the lady of our life. This paper describes the system used for identifying misogynous content using images and text. The system developed by the team TECHSSN uses transformer models to detect the misogynous content from text and Convolutional Neural Network model for image data. Various models like BERT, ALBERT, XLNET and CNN are explored and the combination of ALBERT and CNN as an ensemble model provides better results than the rest. This system was developed for the task 5 of the competition, SemEval 2022.

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TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models
Ramdhanush V | Rajalakshmi Sivanaiah | Angel S | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Irony detection in the social media is an upcoming research which places a main role in sentiment analysis and offensive language identification. Sarcasm is one form of irony that is used to provide intended comments against realism. This paper describes a method to detect intended sarcasm in text (SemEval-2022 Task 6). The TECHSSN team used Bidirectional Encoder Representations from Transformers (BERT) models and its variants to classify the text as sarcastic or non-sarcastic in English and Arabic languages. The data is preprocessed and fed to the model for training. The transformer models learn the weights during the training phase from the given dataset and predicts the output class labels for the unseen test data.

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SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTM
Karun Anantharaman | Divyasri K | Jayannthan Pt | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Task 10 in SemEval 2022 is a composite task which entails analysis of opinion tuples, and recognition and demarcation of their nature. In this paper, we will elaborate on how such a methodology is implemented, how it is undertaken for a Structured Sentiment Analysis, and the results obtained thereof. To achieve this objective, we have adopted a bi-layered BiLSTM approach. In our research, a variation on the norm has been effected towards enhancement of accuracy, by basing the categorization meted out to an individual member as a by-product of its adjacent members, using specialized algorithms to ensure the veracity of the output, which has been modelled to be the holistically most accurate label for the entire sequence.Such a strategy is superior in terms of its parsing accuracy and requires less time. This manner of action has yielded an SF1 of 0.33 in the highest-performing configuration.

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SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models
Shruthi Hariprasad | Sarika Esackimuthu | Saritha Madhavan | Rajalakshmi Sivanaiah | Angel S
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll. Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way. Team SSN_MLRG1 tested and compared results obtained by using three models namely BERT, ALBERT and XLNET. The XLNet model outperformed the other two models in terms of various performance metrics. The proposed XLNet model obtained the 3rd rank in the shared task with a weighted F1-score of 0.558.

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Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil
Varsini S | Kirthanna Rajan | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.

2021

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TECHSSN at SemEval-2021 Task 7: Humor and Offense detection and classification using ColBERT embeddings
Rajalakshmi Sivanaiah | Angel Deborah S | S Milton Rajendram | Mirnalinee Tt | Abrit Pal Singh | Aviansh Gupta | Ayush Nanda
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes the system used for detecting humor in text. The system developed by the team TECHSSN uses binary classification techniques to classify the text. The data undergoes preprocessing and is given to ColBERT (Contextualized Late Interaction over BERT), a modification of Bidirectional Encoder Representations from Transformers (BERT). The model is re-trained and the weights are learned for the dataset. This system was developed for the task 7 of the competition, SemEval 2021.

2020

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TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings
Rajalakshmi Sivanaiah | Angel Suseelan | S Milton Rajendram | Mirnalinee T.t.
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the work of identifying the presence of offensive language in social media posts and categorizing a post as targeted to a particular person or not. The work developed by team TECHSSN for solving the Multilingual Offensive Language Identification in Social Media (Task 12) in SemEval-2020 involves the use of deep learning models with BERT embeddings. The dataset is preprocessed and given to a Bidirectional Encoder Representations from Transformers (BERT) model with pretrained weight vectors. The model is retrained and the weights are learned for the offensive language dataset. We have developed a system with the English language dataset. The results are better when compared to the model we developed in SemEval-2019 Task6.