Arjit Srivastava


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A Multi-Dimensional View of Aggression when voicing Opinion
Arjit Srivastava | Avijit Vajpayee | Syed Sarfaraz Akhtar | Naman Jain | Vinay Singh | Manish Shrivastava
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

The advent of social media has immensely proliferated the amount of opinions and arguments voiced on the internet. These virtual debates often present cases of aggression. While research has been focused largely on analyzing aggression and stance in isolation from each other, this work is the first attempt to gain an extensive and fine-grained understanding of patterns of aggression and figurative language use when voicing opinion. We present a Hindi-English code-mixed dataset of opinion on the politico-social issue of ‘2016 India banknote demonetisation‘ and annotate it across multiple dimensions such as aggression, hate speech, emotion arousal and figurative language usage (such as sarcasm/irony, metaphors/similes, puns/word-play).


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Exploiting Morphological Regularities in Distributional Word Representations
Arihant Gupta | Syed Sarfaraz Akhtar | Avijit Vajpayee | Arjit Srivastava | Madan Gopal Jhanwar | Manish Shrivastava
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set with an accuracy of 85% which is 12% higher than the previous best known system.

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Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems
Syed Sarfaraz Akhtar | Arihant Gupta | Avijit Vajpayee | Arjit Srivastava | Manish Shrivastava
Proceedings of the 11th Linguistic Annotation Workshop

With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.