41 citations · 244 across the 40 of their papers we have counts for
13 papers · 1 filter
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection
Vaibhav Kumar, Mrinal Dhar, Dhruv Khattar +4
In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform…
Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
Ankush Khandelwal, Sahil Swami, Syed Sarfaraz Akhtar +1
The rapid expansion in the usage of social media networking sites leads to a huge amount of unprocessed user generated data which can be used for text mining. Author profiling is t…
Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
Ankush Khandelwal, Sahil Swami, Syed S. Akhtar +1
The tremendous amount of user generated data through social networking sites led to the gaining popularity of automatic text classification in the field of computational linguistic…
Degree based Classification of Harmful Speech using Twitter Data
Sanjana Sharma, Saksham Agrawal, Manish Shrivastava
Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst…
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages
Nurendra Choudhary, Rajat Singh, Manish Shrivastava
Neural network models have shown promising results for text classification. However, these solutions are limited by their dependence on the availability of annotated data. The pros…
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
Sahil Swami, Ankush Khandelwal, Vinay Singh +2
Social media platforms like twitter and facebook have be- come two of the largest mediums used by people to express their views to- wards different topics. Generation of such large…
An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System
Sahil Swami, Ankush Khandelwal, Vinay Singh +2
Social media has become one of the main channels for peo- ple to communicate and share their views with the society. We can often detect from these views whether the person is in f…
Universal Dependency Parsing for Hindi-English Code-switching
Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava +1
Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the bench…
Contrastive Learning of Emoji-based Representations for Resource-Poor Languages
Nurendra Choudhary, Rajat Singh, Ishita Bindlish +1
The introduction of emojis (or emoticons) in social media platforms has given the users an increased potential for expression. We propose a novel method called Classification of Em…
Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages
Nurendra Choudhary, Rajat Singh, Ishita Bindlish +1
Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this…
Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks
Nurendra Choudhary, Rajat Singh, Ishita Bindlish +1
Machine learning approaches in sentiment analysis principally rely on the abundance of resources. To limit this dependence, we propose a novel method called Siamese Network Archite…
Automatic Normalization of Word Variations in Code-Mixed Social Media Text
Rajat Singh, Nurendra Choudhary, Manish Shrivastava
Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend…
Neural Network Architecture for Credibility Assessment of Textual Claims
Nurendra Choudhary, Rajat Singh, Ishita Bindlish +1
Text articles with false claims, especially news, have recently become aggravating for the Internet users. These articles are in wide circulation and readers face difficulty discer…