most citedUnsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings

3 citations · 4 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CL2018

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…

cs.CL2018

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…

cs.CL2018

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…

cs.CL2018

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…

cs.CL20171 cited

An Unsupervised Approach for Mapping between Vector Spaces

Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee +3

We present a language independent, unsupervised approach for transforming word embeddings from source language to target language using a transformation matrix. Our model handles t…

cs.CL20173 cited

Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings

Syed Sarfaraz Akhtar, Arihant Gupta, Avijit Vajpayee +2

We present a language independent, unsupervised method for building word embeddings using morphological expansion of text. Our model handles the problem of data sparsity and yields…