no code implementations • EACL (DravidianLangTech) 2021 • Suman Dowlagar, Radhika Mamidi
In this paper, we propose the graph convolutional networks (GCN) for sentiment analysis on code-mixed text.
no code implementations • ICON 2019 • Shalaka Vaidya, Hiranmai Sri Adibhatla, Radhika Mamidi
To the best of our knowledge, the proposed design is first of its kind which accounts for entire process of comprehending the passage and then answering the questions associated with the passage.
no code implementations • EACL (DravidianLangTech) 2021 • Suman Dowlagar, Radhika Mamidi
In a multilingual society, where code-mixing is a norm, the hate content would be delivered in a code-mixed form in social media, which makes the offensive content identification, further challenging.
no code implementations • CODI 2021 • Vaishnavi Pamulapati, Radhika Mamidi
(a) develop a conversational (humorous and non-humorous) dataset in Telugu.
no code implementations • LTEDI (ACL) 2022 • Suman Dowlagar, Radhika Mamidi
Later, we used machine learning methods to train and detect the signs of depression.
no code implementations • NAACL (CALCS) 2021 • Suman Dowlagar, Radhika Mamidi
Code-Switching is the embedding of linguistic units or phrases from two or more languages in a single sentence.
no code implementations • NAACL (CALCS) 2021 • Dama Sravani, Lalitha Kameswari, Radhika Mamidi
Political discourse is one of the most interesting data to study power relations in the framework of Critical Discourse Analysis.
no code implementations • ICON 2020 • Meghana Bommadi, Shreya Terupally, Radhika Mamidi
Question Answer pair generation is a task that has been worked upon by multiple researchers in many languages.
no code implementations • ICON 2020 • Salil Aggarwal, Abhigyan Ghosh, Radhika Mamidi
To the best of our knowledge, this is the first corpus of Hindi shayaris annotated with sentiment polarity information.
no code implementations • COLING (LAW) 2020 • Vaishnavi Pamulapati, Gayatri Purigilla, Radhika Mamidi
Humor research is a multifaceted field that has led to a better understanding of humor’s psychological effects and the development of different theories of humor.
no code implementations • SemEval (NAACL) 2022 • Samyak Agrawal, Radhika Mamidi
In current times, memes have become one of the most popular mediums to share jokes and information with the masses over the internet.
no code implementations • SemEval (NAACL) 2022 • Samyak Agrawal, Radhika Mamidi
For the second sub-task, given a paragraph, we have to find which PCL categories express the condescension.
no code implementations • LTEDI (ACL) 2022 • Ishan Sanjeev Upadhyay, KV Aditya Srivatsa, Radhika Mamidi
Hateful and offensive content on social media platforms can have negative effects on users and can make online communities more hostile towards certain people and hamper equality, diversity and inclusion.
no code implementations • EACL (LTEDI) 2021 • Ishan Sanjeev Upadhyay, Nikhil E, Anshul Wadhawan, Radhika Mamidi
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset.
1 code implementation • ACL (WOAH) 2021 • Ravsimar Sodhi, Kartikey Pant, Radhika Mamidi
Online abuse and offensive language on social media have become widespread problems in today’s digital age.
no code implementations • EACL (LTEDI) 2021 • Suman Dowlagar, Radhika Mamidi
Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world.
no code implementations • EACL (LTEDI) 2021 • Sunil Gundapu, Radhika Mamidi
The rapid rise of online social networks like YouTube, Facebook, Twitter allows people to express their views more widely online.
no code implementations • NAACL (SocialNLP) 2022 • Ishan Sanjeev Upadhyay, KV Aditya Srivatsa, Radhika Mamidi
Over the past few years, there has been a growing concern around toxic positivity on social media which is a phenomenon where positivity is used to minimize one’s emotional experience.
no code implementations • ACL 2022 • Suma Reddy Duggenpudi, Subba Reddy Oota, Mounika Marreddy, Radhika Mamidi
Our contributions in this paper include (i) Two annotated NER datasets for the Telugu language in multiple domains: Newswire Dataset (ND) and Medical Dataset (MD), and we combined ND and MD to form Combined Dataset (CD) (ii) Comparison of the finetuned Telugu pretrained transformer models (BERT-Te, RoBERTa-Te, and ELECTRA-Te) with other baseline models (CRF, LSTM-CRF, and BiLSTM-CRF) (iii) Further investigation of the performance of Telugu pretrained transformer models against the multilingual models mBERT, XLM-R, and IndicBERT.
no code implementations • ACL 2022 • Samyak Agrawal, Kshitij Gupta, Devansh Gautam, Radhika Mamidi
Political propaganda in recent times has been amplified by media news portals through biased reporting, creating untruthful narratives on serious issues causing misinformed public opinions with interests of siding and helping a particular political party.
no code implementations • RANLP 2021 • Venkata Himakar Yanamandra, Kartikey Pant, Radhika Mamidi
We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products’ sentiment.
no code implementations • RANLP 2021 • Siva Subrahamanyam Varma Kusampudi, Preetham Sathineni, Radhika Mamidi
In a multilingual society, people communicate in more than one language, leading to Code-Mixed data.
no code implementations • RANLP 2021 • Siva Subrahamanyam Varma Kusampudi, Anudeep Chaluvadi, Radhika Mamidi
In the current era of machine learning, a common problem to the above-mentioned tasks is the availability of Learning data to train models.
no code implementations • RANLP 2021 • Lalitha Kameswari, Radhika Mamidi
We create a ranking of these techniques based on their contribution to bias.
no code implementations • RANLP 2021 • Suman Dowlagar, Radhika Mamidi
We train a joint learning method by combining POS tagging and LI models on code-mixed social media text obtained from the ICON shared task.
no code implementations • RANLP 2021 • Vaibhav Bajaj, Kartikey Pant, Ishan Upadhyay, Srinath Nair, Radhika Mamidi
Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • RANLP 2021 • Salil Aggarwal, Sourav Kumar, Radhika Mamidi
Can a classification model trained on one Indian language be reused for other Indian languages?
no code implementations • ACL (dialdoc) 2021 • Meghana Bommadi, Shreya Terupally, Radhika Mamidi
We built our learning assistant for Telugu language to help with teaching in the mother tongue, which is the most efficient way of learning.
no code implementations • ICON 2020 • Sunil Gundapu, Radhika Mamidi
With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing.
no code implementations • EAMT 2020 • Allen Antony, Arghya Bhattacharya, Jaipal Goud, Radhika Mamidi
Sentiment analysis is a widely researched NLP problem with state-of-the-art solutions capable of attaining human-like accuracies for various languages.
no code implementations • 18 Mar 2024 • Patanjali Bhamidipati, Advaith Malladi, Manish Shrivastava, Radhika Mamidi
In recent studies, the extensive utilization of large language models has underscored the importance of robust evaluation methodologies for assessing text generation quality and relevance to specific tasks.
1 code implementation • 24 Feb 2024 • Ayan Datta, Aryan Chandramania, Radhika Mamidi
This document contains the details of the authors' submission to the proceedings of SemEval 2024's Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection Subtask A (monolingual) and B.
1 code implementation • 25 Dec 2022 • Lakshmi Sireesha Vakada, Anudeep Ch, Mounika Marreddy, Subba Reddy Oota, Radhika Mamidi
Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages.
no code implementations • 24 Nov 2022 • Tanish Lad, Himanshu Maheshwari, Shreyas Kottukkal, Radhika Mamidi
Following this standard supervised fine-tuning is done for different downstream tasks.
no code implementations • SemEval (NAACL) 2022 • Suman Dowlagar, Radhika Mamidi
Our work consists of Named Entity Recognition (NER) on the code-mixed dataset by leveraging the multilingual data.
1 code implementation • 7 Jun 2022 • Kshitij Gupta, Devansh Gautam, Radhika Mamidi
We propose a pipeline that utilizes English-only vision-language models to train a monolingual model for a target language.
no code implementations • 3 May 2022 • Sunil Gundapu, Radhika Mamidi
The exponential rise of social media networks has allowed the production, distribution, and consumption of data at a phenomenal rate.
1 code implementation • 2 May 2022 • Mounika Marreddy, Subba Reddy Oota, Lakshmi Sireesha Vakada, Venkata Charan Chinni, Radhika Mamidi
Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc.
no code implementations • 9 Apr 2022 • Sai Kiran Gorthi, Radhika Mamidi
Computational Paninian Grammar model helps in decoding a natural language expression as a series of modifier-modified relations and therefore facilitates in identifying dependency relations closer to language (context) semantics compared to the usual Stanford dependency relations.
no code implementations • ACL 2021 • Sourav Kumar, Salil Aggarwal, Dipti Misra Sharma, Radhika Mamidi
India is one of the most linguistically diverse nations of the world and is culturally very rich.
no code implementations • SEMEVAL 2021 • Tathagata Raha, Ishan Sanjeev Upadhyay, Radhika Mamidi, Vasudeva Varma
This paper describes our approach (IIITH) for SemEval-2021 Task 5: HaHackathon: Detecting and Rating Humor and Offense.
1 code implementation • SEMEVAL 2021 • Kshitij Gupta, Devansh Gautam, Radhika Mamidi
Memes are one of the most popular types of content used to spread information online.
1 code implementation • Workshop on Asian Translation 2021 • Kshitij Gupta, Devansh Gautam, Radhika Mamidi
Multimodal Machine Translation (MMT) enriches the source text with visual information for translation.
Ranked #1 on Multimodal Machine Translation on Hindi Visual Genome (Test Set) (using extra training data)
no code implementations • 27 Apr 2021 • Manojit Chakraborty, Shubham Das, Radhika Mamidi
Social Media Platforms (SMPs) like Facebook, Twitter, Instagram etc.
no code implementations • 28 Feb 2021 • Tanishq Chaudhary, Mayank Goel, Radhika Mamidi
Well-defined jokes can be divided neatly into a setup and a punchline.
no code implementations • 24 Feb 2021 • Ishan Sanjeev Upadhyay, Nikhil E, Anshul Wadhawan, Radhika Mamidi
Our solution got a weighted F1 score of 0. 93, 0. 75 and 0. 49 for English, Malayalam and Tamil respectively.
no code implementations • 24 Feb 2021 • Sunil Gundapu, Radhika Mamidi
With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing.
no code implementations • EACL (WASSA) 2021 • Anvesh Rao Vijjini, Kaveri Anuranjana, Radhika Mamidi
Our axes of analysis include Task difficulty on CL, comparing CL pacing techniques, and qualitative analysis by visualizing the movement of attention scores in the model as curriculum phases progress.
1 code implementation • 22 Jan 2021 • Suman Dowlagar, Radhika Mamidi
Hateful and Toxic content has become a significant concern in today's world due to an exponential rise in social media.
1 code implementation • 22 Jan 2021 • Suman Dowlagar, Radhika Mamidi
We used the proposed method for the Task: "Sentiment Analysis for Dravidian Languages in Code-Mixed Text", and it achieved an F1 score of $0. 58$ and $0. 66$ for the given Dravidian code mixed data sets.
no code implementations • ICON 2020 • Suman Dowlagar, Radhika Mamidi
These features increase the complexity of the classification algorithm.
no code implementations • ICON 2020 • Suman Dowlagar, Radhika Mamidi
Terminology extraction, also known as term extraction, is a subtask of information extraction.
no code implementations • ICON 2020 • Suman Dowlagar, Radhika Mamidi
In this paper, we present a transfer learning system to perform technical domain identification on multilingual text data.
2 code implementations • 1 Jan 2021 • Sunil Gundapu, Radhika Mamidi
For our analysis in this paper, we report a methodology to analyze the reliability of information shared on social media pertaining to the COVID-19 pandemic.
no code implementations • SEMEVAL 2020 • Sunil Gundapu, Radhika Mamidi
Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication.
no code implementations • SEMEVAL 2020 • Sunil Gundapu, Radhika Mamidi
Our system first generates two types of embeddings for the social media text.
no code implementations • PACLIC 2018 • Sunil Gundapu, Radhika Mamidi
In a multilingual or sociolingual configuration Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays.
no code implementations • WS 2020 • Adithya Avvaru, Sanath Vobilisetty, Radhika Mamidi
Sarcasm detection, regarded as one of the sub-problems of sentiment analysis, is a very typical task because the introduction of sarcastic words can flip the sentiment of the sentence itself.
no code implementations • WS 2020 • Lalitha Kameswari, Dama Sravani, Radhika Mamidi
Usage of presuppositions in social media and news discourse can be a powerful way to influence the readers as they usually tend to not examine the truth value of the hidden or indirectly expressed information.
no code implementations • 1 Jun 2020 • Tanvi Dadu, Kartikey Pant, Radhika Mamidi
There is a growing interest in understanding how humans initiate and hold conversations.
Ranked #1 on Text Classification on AffCon 2020 Emotion Detection
1 code implementation • 10 May 2020 • Vijjini Anvesh Rao, Kaveri Anuranjana, Radhika Mamidi
In this paper, we apply the ideas of curriculum learning, driven by SentiWordNet in a sentiment analysis setting.
no code implementations • LREC 2020 • Gaurav Mohanty, Pruthwik Mishra, Radhika Mamidi
Given the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences and made it publicly available to promote research in the field.
no code implementations • LREC 2020 • Lalitha Kameswari, Radhika Mamidi
In today{'}s era of globalisation, the increased outreach for every event across the world has been leading to conflicting opinions, arguments and disagreements, often reflected in print media and online social platforms.
no code implementations • LREC 2020 • Yashwanth Reddy Regatte, Rama Rohit Reddy Gangula, Radhika Mamidi
Aspect Based Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • European Conference on Information Retrieval 2020 • Kartikey Pant, Yash Verma, Radhika Mamidi
We address this issue by providing a simple framework for encoding sentiment-specific information in the target sentence while preserving the content information.
Ranked #1 on Text Style Transfer on Yelp Review Dataset (Large)
1 code implementation • 16 Feb 2020 • Tanvi Dadu, Kartikey Pant, Radhika Mamidi
Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization.
Ranked #1 on Bias Detection on Wiki Neutrality Corpus
no code implementations • 25 Nov 2019 • Elizabeth Jasmi George, Radhika Mamidi
But in human-computer interactions, the machine fails to understand the implicated meaning unless it is trained with a dataset containing the implicated meaning of an utterance along with the utterance and the context in which it is uttered.
no code implementations • 22 Nov 2019 • Vinay Annam, Nikhil Koditala, Radhika Mamidi
Anaphora resolution is a challenging task which has been the interest of NLP researchers for a long time.
no code implementations • WS 2019 • Suma Reddy Duggenpudi, Kusampudi Siva Subrahamanyam Varma, Radhika Mamidi
In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built.
no code implementations • 18 Oct 2019 • Elizabeth Jasmi George, Radhika Mamidi
Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it.
1 code implementation • WS 2019 • Kartikey Pant, Venkata Himakar Yanamandra, Alok Debnath, Radhika Mamidi
Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services.
no code implementations • WS 2019 • Rama Rohit Reddy Gangula, Suma Reddy Duggenpudi, Radhika Mamidi
Language is a powerful tool which can be used to state the facts as well as express our views and perceptions.
no code implementations • 20 Jun 2019 • Kaveri Anuranjana, Vijjini Anvesh Rao, Radhika Mamidi
We present a rule-based system for question generation in Hindi by formalizing question transformation methods based on karaka-dependency theory.
no code implementations • WS 2019 • Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, Radhika Mamidi
We apply our approach on Hindi-English code-mixed corpus against the target entity - {``}Demonetisation.
no code implementations • WS 2019 • Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, Radhika Mamidi
We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures.
no code implementations • PACLIC 2018 • Subba Reddy Oota, Adithya Avvaru, Mounika Marreddy, Radhika Mamidi
We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT).
1 code implementation • COLING 2018 • Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis.
no code implementations • ACL 2018 • Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using word-level sentiment annotations.
no code implementations • 4 Jul 2018 • Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
In this paper, we discuss the enrichment of a manually developed resource of Telugu lexicon, OntoSenseNet.
no code implementations • ACL 2018 • Pravallika Etoori, Manoj Chinnakotla, Radhika Mamidi
Spelling correction is a well-known task in Natural Language Processing (NLP).
no code implementations • ACL 2018 • Nikhilesh Bhatnagar, Manish Shrivastava, Radhika Mamidi
Natural Language Generation (NLG) is a research task which addresses the automatic generation of natural language text representative of an input non-linguistic collection of knowledge.
no code implementations • WS 2018 • Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
In this paper, we discuss the enrichment of a manually developed resource, OntoSenseNet for Telugu.
no code implementations • 11 Jun 2018 • Srishti Aggarwal, Kritik Mathur, Radhika Mamidi
In particular, we consider code-mixed puns which have become increasingly mainstream on social media, in informal conversations and advertisements and aim to build a system which can automatically identify the pun location and recover the target of such puns.
no code implementations • 11 Jun 2018 • Gangula Rama Rohit Reddy, Radhika Mamidi
The song lyric is a rich source of datasets containing words that are helpful in analysis and classification of sentiments generated from it.
no code implementations • 29 May 2018 • Vishal Batchu, Varshit Battu, Murali Krishna Reddy, Radhika Mamidi
A straightforward approach would be to predict the scores of video games based on other information related to the game.
no code implementations • 15 Apr 2018 • Radhika Mamidi
Contextual knowledge is the most important element in understanding language.
no code implementations • WS 2017 • S Mukku, eep Sricharan, Radhika Mamidi
The corpus, named ACTSA (Annotated Corpus for Telugu Sentiment Analysis) has a collection of Telugu sentences taken from different sources which were then pre-processed and manually annotated by native Telugu speakers using our annotation guidelines.
no code implementations • WS 2017 • Gaurav Mohanty, Abishek Kannan, Radhika Mamidi
One solution is to use available resources in English and translate the final source lexicon to target lexicon via machine translation.
1 code implementation • WS 2017 • Akshita Jha, Radhika Mamidi
Our work helps analyze and understand the much prevalent ambivalent sexism in social media.
no code implementations • 24 May 2016 • Nikhilesh Bhatnagar, Radhika Mamidi
We posit the construction of a sentence as a highly restricted sequence of such templates.
no code implementations • 11 Apr 2016 • Arnav Sharma, Sakshi Gupta, Raveesh Motlani, Piyush Bansal, Manish Srivastava, Radhika Mamidi, Dipti M. Sharma
In this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed.