Search Results for author:
Found 12 papers, 3 papers with code
A Multi-Dimensional View of Aggression when voicing Opinion
The advent of social media has immensely proliferated the amount of opinions and arguments voiced on the internet.
Aggression Detection on Social Media Text Using Deep Neural Networks
In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users.
Named Entity Recognition for Hindi-English Code-Mixed Social Media Text
Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction.
Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
In this paper, we analyze the task of author's gender prediction in code-mixed content and present a corpus of English-Hindi texts collected from Twitter which is annotated with author's gender.
A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection
Hate speech detection in social media texts is an important Natural language Processing task, which has several crucial applications like sentiment analysis, investigating cyberbullying and examining socio-political controversies.
Corpus Creation and Emotion Prediction for Hindi-English Code-Mixed Social Media Text
Emotion Prediction is a Natural Language Processing (NLP) task dealing with detection and classification of emotions in various monolingual and bilingual texts.
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
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.
An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System
Social media has become one of the main channels for peo- ple to communicate and share their views with the society.
An Unsupervised Approach for Mapping between Vector Spaces
Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target language by relying on transformed embeddings of words of the source language.
Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings
We evaluate our method using small sized training sets on eleven test sets for the word similarity task across seven languages.
Exploiting Morphological Regularities in Distributional Word Representations
We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces.
Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems
With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations.
