Ayush Kumar

Also published as: Kumar Ayush


2020

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BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text
Ayush Kumar | Harsh Agarwal | Keshav Bansal | Ashutosh Modi
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population. In this paper, we present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets. While the CNN component helps in the classification of positive and negative tweets, the self-attention based LSTM, helps in the classification of neutral tweets, because of its ability to identify correct sentiment among multiple sentiment bearing units. We achieved F1 scores of 0.707 (ranked 5th) and 0.725 (ranked 13th) on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively. The submissions for Hinglish and Spanglish tasks were made under the usernames ayushk and harsh_6 respectively.

2016

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A Hybrid Deep Learning Architecture for Sentiment Analysis
Md Shad Akhtar | Ayush Kumar | Asif Ekbal | Pushpak Bhattacharyya
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification. We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature we also evaluate it on two benchmark English datasets. Evaluation shows that the results of the proposed method are consistent across all the datasets and often outperforms the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for less-resourced languages such as Hindi.

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OCR++: A Robust Framework For Information Extraction from Scholarly Articles
Mayank Singh | Barnopriyo Barua | Priyank Palod | Manvi Garg | Sidhartha Satapathy | Samuel Bushi | Kumar Ayush | Krishna Sai Rohith | Tulasi Gamidi | Pawan Goyal | Animesh Mukherjee
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper proposes OCR++, an open-source framework designed for a variety of information extraction tasks from scholarly articles including metadata (title, author names, affiliation and e-mail), structure (section headings and body text, table and figure headings, URLs and footnotes) and bibliography (citation instances and references). We analyze a diverse set of scientific articles written in English to understand generic writing patterns and formulate rules to develop this hybrid framework. Extensive evaluations show that the proposed framework outperforms the existing state-of-the-art tools by a large margin in structural information extraction along with improved performance in metadata and bibliography extraction tasks, both in terms of accuracy (around 50% improvement) and processing time (around 52% improvement). A user experience study conducted with the help of 30 researchers reveals that the researchers found this system to be very helpful. As an additional objective, we discuss two novel use cases including automatically extracting links to public datasets from the proceedings, which would further accelerate the advancement in digital libraries. The result of the framework can be exported as a whole into structured TEI-encoded documents. Our framework is accessible online at http://www.cnergres.iitkgp.ac.in/OCR++/home/.

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IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis
Ayush Kumar | Sarah Kohail | Amit Kumar | Asif Ekbal | Chris Biemann
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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IITPSemEval: Sentiment Discovery from 140 Characters
Ayush Kumar | Vamsi Krishna | Asif Ekbal
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)