Himanshu Sharad Bhatt


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Learning Transferable Feature Representations Using Neural Networks
Himanshu Sharad Bhatt | Shourya Roy | Arun Rajkumar | Sriranjani Ramakrishnan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Learning representations such that the source and target distributions appear as similar as possible has benefited transfer learning tasks across several applications. Generally it requires labeled data from the source and only unlabeled data from the target to learn such representations. While these representations act like a bridge to transfer knowledge learned in the source to the target; they may lead to negative transfer when the source specific characteristics detract their ability to represent the target data. We present a novel neural network architecture to simultaneously learn a two-part representation which is based on the principle of segregating source specific representation from the common representation. The first part captures the source specific characteristics while the second part captures the truly common representation. Our architecture optimizes an objective function which acts adversarial for the source specific part if it contributes towards the cross-domain learning. We empirically show that two parts of the representation, in different arrangements, outperforms existing learning algorithms on the source learning as well as cross-domain tasks on multiple datasets.


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Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification
Raksha Sharma | Pushpak Bhattacharyya | Sandipan Dandapat | Himanshu Sharad Bhatt
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the significance of a word to express an opinion often differ from one domain to another domain. Owing to these differences, cross-domain sentiment classification is still a challenging task. In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification. We present a novel approach based on χ2 test and cosine-similarity between context vector of words to identify polarity preserving significant words across domains. Furthermore, we show that a weighted ensemble of the classifiers enhances the cross-domain classification performance.


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SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization
Himanshu Sharad Bhatt | Sandipan Dandapat | Peddamuthu Balaji | Shourya Roy | Sharmistha Jat | Deepali Semwal
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Cross-domain Text Classification with Multiple Domains and Disparate Label Sets
Himanshu Sharad Bhatt | Manjira Sinha | Shourya Roy
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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An Iterative Similarity based Adaptation Technique for Cross-domain Text Classification
Himanshu Sharad Bhatt | Deepali Semwal | Shourya Roy
Proceedings of the Nineteenth Conference on Computational Natural Language Learning