The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024. There are two sub tasks: Task 1 -The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model – SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro f1-score 0.3517 in test data and a rank 6.

Wordwizard Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024

Sentiment Analysis of Dravidian Languages has begun to garner attention recently as there is more need to analyze emotional responses and subjective opinions present in social media text. As this data is code-mixed and there are not many solutions to code-mixed text out there, we present to you a stellar solution to DravidianLangTech@EACL 2024: Sentiment Analysis in Tamil and Tulu task. To understand the sentiment of social media text, we used pre trained transformer models and feature extraction vectorizers to classify the data with results that placed us 11th in the rankings for the Tamil task and 8th for the Tulu task with a macro F1 score of 0.074 and 0.251 which shows the efficiency of our approach.

WordWizardsDravidianLangTech@EACL 2024: Sentiment Analysis in Tamil and Tulu

Hate speech refers to the offensive remarks against a community or individual based on inherent characteristics. Hate speech against a community based on their caste and native are unfortunately prevalent in the society. Especially with social media platforms being a very popular tool for communication and sharing ideas, people post hate speech against caste or immigrants on social medias. The Shared Task LT–EDI 2024: Caste/Immigration Hate Speech Detection\cite{chakravarthi-caste-2024-overview} was created with the objective to create an automatic classification system that detects and classifies hate speech posted on social media targeting a community belonging to a particular caste and immigrants. Datasets in Tamil language were provided along with the shared task. We experimented with several traditional models such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier and Decision Tree Classifier out of which Support Vector Machine yielded the best results placing us 8th in the rank list released by the organizers.

Quartet@LT-EDI 2024: A Support Vector Machine Approach For Caste/Immigration Hate Speech Detection

Meme is a very popular term prevailing among almost all social media platforms in recent days. A meme can be a combination of text and image whose sole purpose is meant to be funny and entertain people. Memes can sometimes promote misogynistic content expressing hatred, contempt or prejudice against women. The Shared Task LT–EDI 2024: Multitask Meme classification – Unraveling Misogynistic and Trolls in Online Memes Task 1 was created with the purpose to classify social media memes as "Misogynistic" and "Non - Misogynistic". The task encompassed Tamil and Malayalam datasets. We separately classified the textual data using Multinomial Naive Bayes and pictorial data using ResNet50 model. The results of from both data were combined to yield an overall result. We were ranked 2nd for both languages in this task.

Quartet@LT-EDI 2024: A SVM-ResNet50 Approach For Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes

Social media is considered as a platform where users express themselves. The rise of social media as one of humanity’s most important public communication platforms presents a potential prospect for early identification and management of mental illness. Depression is one such illness that can lead to a variety of emotional and physical problems. It is necessary to measure the level of depression from the social media text to treat them and to avoid the negative consequences. Detecting levels of depression is a challenging task since it involves the mindset of the people, which can change periodically. The aim of the DepSign-LT-EDI@ACL-2022 shared task is to classify the social media text into three levels of depression namely “Not Depressed”, “Moderately Depressed”, and “Severely Depressed”. This overview presents a description of the task, the data set, methodologies used and an analysis on the results of the submissions. The models that were submitted as a part of the shared task had used a variety of technologies from traditional machine learning algorithms to deep learning models. It could be observed from the result that the transformer based models have outperformed the other models. Among the 32 teams who had submitted their results for the shared task, the best macro F1-score of 0.583 was obtained using a transformer based model.

Findings of the Shared Task on Detecting Signs of Depression from Social Media

Homophobia and Transphobia Detection is the task of identifying homophobia, transphobia, and non-anti-LGBT+ content from the given corpus. Homophobia and transphobia are both toxic languages directed at LGBTQ+ individuals that are described as hate speech. This paper summarizes our findings on the "Homophobia and Transphobia Detection in social media comments" shared task held at LT-EDI 2022 - ACL 2022. This shared task focused on three sub-tasks for Tamil, English, and Tamil-English (code-mixed) languages. It received 10 systems for Tamil, 13 systems for English, and 11 systems for Tamil-English. The best systems for Tamil, English, and Tamil-English scored 0.570, 0.870, and 0.610, respectively, on average macro F1-score.

Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments

As the world around us continues to become increasingly digital, it has been acknowledged that there is a growing need for emotion analysis of social media content. The task of identifying the emotion in a given text has many practical applications ranging from screening public health to business and management. In this paper, we propose a language-agnostic model that focuses on emotion analysis in Tamil text. Our experiments yielded an F1 score of 0.010.

Emotion Analysis in Tamil Text using Language Agnostic Embeddings

Abusive language has lately been prevalent in comments on various social media platforms. The increasing hostility observed on the internet calls for the creation of a system that can identify and flag such acerbic content, to prevent conflict and mental distress. This task becomes more challenging when low-resource languages like Tamil, as well as the oftenobserved Tamil-English code-mixed text, are involved. The approach used in this paper for the classification model includes different methods of feature extraction and the use of traditional classifiers. We propose a novel method of combining language-agnostic sentence embeddings with the TF-IDF vector representation that uses a curated corpus of words as vocabulary, to create a custom embedding, which is then passed to an SVM classifier. Our experimentation yielded an accuracy of 52% and a macro F1-score of 0.54.

Mixed Data Using Custom Embeddings with LaBSE

The social media is one of the significant digital platforms that create a huge impact in peoples of all levels. The comments posted on social media is powerful enough to even change the political and business scenarios in very few hours. They also tend to attack a particular individual or a group of individuals. This shared task aims at detecting the abusive comments involving, Homophobia, Misandry, Counter- speech, Misogyny, Xenophobia, Transphobic. The hope speech is also identified. A dataset collected from social media tagged with the above said categories in Tamil and Tamil-English code-mixed languages are given to the participants. The participants used different machine learning and deep learning algorithms. This paper presents the overview of this task comprising the dataset details and results of the participants.

Overview of Abusive Comment Detection in Tamil-ACL 2022

Detecting funniness level using traditional learning with sentence embeddings

[**Teleport to Gather**](https://app.gather.town/app/XDzSQW1b0mpTTAYr/EACL2024?spawnToken=XxkeWnX_QkG6G-llIfSi) The development of technology increases our internet use, and most of the global languages have adapted themselves to the digital era. However, there are many regional, under-resourced languages that face challenges as they still lack developments in language technology [1]. One such language family is the Dravidian family of languages. Dravidian languages are primarily spoken in south India and Sri Lanka. Pockets of speakers are found in Nepal, Pakistan, Malaysia, other parts of India and elsewhere in the world. The Dravidian languages, which are 4,500 years old [2] and spoken by millions of speakers, are under-resourced in speech and natural language processing [1]. The Dravidian languages are divided into four groups: South, South-Central, Central, and North groups. Dravidian morphology is agglutinating and exclusively suffixal. Syntactically, Dravidian languages are head-final and left-branching. They are free-constituent order languages. To improve access to and production of information for monolingual speakers of Dravidian languages, it is necessary to have speech and languages technologies. The aim of these workshops is to save the Dravidian languages from extinction in technology. This is the first workshop on speech and language technologies for Dravidian languages. 
 [**Read more**](https://sites.google.com/view/dravidianlangtech-2024/)
 **Program Overview** 
09:15 - 09:30 Opening Remarks 
09:30 - 10:00 Keynote 
10:00 - 11:00 Developing Models and Resources for Low Resource Applications - Chair: Bharathi Raja Chakravarthi, University of Galway, Ireland 
11:00 - 13:00 Poster Session - Chair: Prasanna Kumar Kumerasan, University of Galway, Ireland 
13:00 - 13:30 Lunch Break 
13:30 - 15:45 Overview and Findings of Shared Tasks at DravidianLangTech 2024 - Chair: Rahul Ponnusamy, University of Galway, Ireland 
15:45 - 17:00 Tea Break 
17:00 - 17:15 Meeting, Awards, Closing [**Detailed Program**](https://sites.google.com/view/dravidianlangtech-2024/schedule)

W19: The Fourth Workshop on Speech and Language Technologies for Dravidian Languages (DravidianLangTech)

workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

EACL 2024

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

[**Teleport to Gather**](https://app.gather.town/app/XDzSQW1b0mpTTAYr/EACL2024?spawnToken=QvCZ0RrQRM6hvHqVsTqW) Equality, Diversity and Inclusion (EDI) is an important agenda across every field throughout the world. Language as a major part of communication should be inclusive and treat everyone with equality. Today’s large internet community uses language technology (LT) and has a direct impact on people across the globe. EDI is crucial to ensure everyone is valued and included, so it is necessary to build LT that serves this purpose. Recent results have shown that big data and deep learning are entrenching existing biases and that some algorithms are even naturally biased due to problems such as ‘regression to the mode’. Our focus is on creating LT that will be more inclusive of gender, racial, sexual orientation, persons with disability. The workshop will focus on creating speech and language technology to address EDI not only in English, but also in less resourced languages. [**Read more**](https://sites.google.com/view/lt-edi-2024) [**Invited Speaker**](https://sites.google.com/view/lt-edi-2024/home) **Program Overview** 
09:00 - 09:15 Opening Remarks 
09:15 - 09:45 Keynote - Chair: Bharathi Raja Chakravarthi, University of Galway, Ireland 
09:45 - 11:00 Paper Session 1 - Chair: Bharathi Raja Chakravarthi, University of Galway, Ireland 
11:00 - 11:30 Cofee Break 
11:30 - 12:30 Poster Session 1 - Chair: B. Bharathi, SSN College of Engineering, India 
12:30 - 13:30 Lunch Break 
13:30 - 15:45 Paper Session 2 - Chair: Miguel Angel Garcia Cumbreras, University of Jaén, Spain 
15:45 - 16:15 Coffee Break 
16:15 - 17:15 Poster Session 2 - Chair: Gyorgy Kovacs,Lulea University of Technology (LTU), Sweden 
17:15 - 17:45 Plenary and Closing Remarks [**Detailed Program**](https://sites.google.com/view/lt-edi-2024/schedule)

W3: Fourth Workshop on Language Technology for Equality, Diversity and Inclusion (LTEDI)

**Organizers:** Bharathi Raja Chakravarthi, Bharathi B, John P. McCrae, Manel Zarrouk, Kalika Bali, Paul
Buitelaar
 **Description:** Equality, Diversity and Inclusion (EDI) is an important agenda across every field throughout the world. Language as a
major part of communication should be inclusive and treat everyone with equality. Today’s large internet community
uses language technology (LT) and has a direct impact on people across the globe. EDI is crucial to ensure everyone is
valued and included, so it is necessary to build LT that serves this purpose. Recent results have shown that big data and
deep learning are entrenching existing biases and that some algorithms are even naturally biased due to problems such
as ‘regression to the mode’. Our focus is on creating LT that will be more inclusive of gender, racial, sexual orientation,
persons with disability. The workshop will focus on creating speech and language technology to address EDI not only in
English, but also in less resourced languages.
 
**Please visit our [website](https://sites.google.com/view/lt-edi-2022/home)**

(LT-EDI) The Second Workshop on Language Technology for Equality, Diversity and Inclusion

findings / work in progress

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

**Organizers:** Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Parameswari
Krishnamurthy, Elizabeth Sherly, Sinnathamby Mahesan
 **Description:** The development of technology increases our internet use, and most of the global languages have adapted themselves
to the digital era. However, there are many regional, under-resourced languages that face challenges as they still lack
developments in language technology [1]. One such language family is the Dravidian family of languages. Dravidian
languages are primarily spoken in south India and Sri Lanka. Pockets of speakers are found in Nepal, Pakistan, Malaysia,
other parts of India and elsewhere in the world. The Dravidian languages, which are 4,500 years old [2] and spoken by
millions of speakers, are under-resourced in speech and natural language processing [1]. The Dravidian languages are
divided into four groups: South, South-Central, Central, and North groups. Dravidian morphology is agglutinating and
exclusively suffixal. Syntactically, Dravidian languages are head-final and left-branching. They are free-constituent order
languages. To improve access to and production of information for monolingual speakers of Dravidian languages, it is
necessary to have speech and languages technologies. The aim of these workshops is to save the Dravidian languages
from extinction in technology. This is the first workshop on speech and language technologies for Dravidian languages.
 
**Please visit our [website](https://dravidianlangtech.github.io/2022/)**

(Dravidian LangTech) The Second Workshop on Speech and Language Technologies for DravidianLanguages

SemEval (Semantic Evaluation) is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense evaluation series. The evaluations are intended to explore the nature of meaning in language. While meaning is intuitive to humans, transferring those intuitions to computational analysis has proved elusive.

This series of evaluations is providing a mechanism to characterize in more precise terms exactly what is necessary to compute in meaning. As such, the evaluations provide an emergent mechanism to identify the problems and solutions for computations with meaning. These exercises have evolved to articulate more of the dimensions that are involved in our use of language. They began with apparently simple attempts to identify word senses computationally. They have evolved to investigate the interrelationships among the elements in a sentence (e.g., semantic role labeling), relations between sentences (e.g., coreference), and the nature of what we are saying (semantic relations and sentiment analysis).

The purpose of the SemEval and Senseval exercises is to evaluate semantic analysis systems. "Semantic Analysis" refers to a formal analysis of meaning, and "computational" refer to approaches that in principle support effective implementation.

The first three evaluations, Senseval-1 through Senseval-3, were focused on word sense disambiguation (WSD), each time growing in the number of languages offered in the tasks and in the number of participating teams. Beginning with the fourth workshop, SemEval-2007 (SemEval-1), the nature of the tasks evolved to include semantic analysis tasks outside of word sense disambiguation.

Triggered by the conception of the STARSEM conference, the SemEval community had decided to hold the evaluation workshops yearly in association with the STARSEM conference. It was also the decision that not every evaluation task will be run every year, e.g. none of the WSD tasks were included in the SemEval-2012 workshop.





International Workshop on Semantic Evaluation (SemEval)

poster

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Durairaj Thenmozhi

10

3

SHORT BIO

Presentations

Wordwizard Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024

WordWizardsDravidianLangTech@EACL 2024: Sentiment Analysis in Tamil and Tulu

Quartet@LT-EDI 2024: A Support Vector Machine Approach For Caste/Immigration Hate Speech Detection

Quartet@LT-EDI 2024: A SVM-ResNet50 Approach For Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes

Findings of the Shared Task on Detecting Signs of Depression from Social Media

Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments

Emotion Analysis in Tamil Text using Language Agnostic Embeddings

Mixed Data Using Custom Embeddings with LaBSE

Overview of Abusive Comment Detection in Tamil-ACL 2022

Detecting funniness level using traditional learning with sentence embeddings

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PRESENTATIONS

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