Sutanu Chakraborti


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Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting
Aakash Srinivasan | Harshavardhan Kamarthi | Devi Ganesan | Sutanu Chakraborti
Proceedings of the 16th International Conference on Natural Language Processing

Neural network based word embeddings, such as Word2Vec and Glove, are purely data driven in that they capture the distributional information about words from the training corpus. Past works have attempted to improve these embeddings by incorporating semantic knowledge from lexical resources like WordNet. Some techniques like retrofitting modify word embeddings in the post-processing stage while some others use a joint learning approach by modifying the objective function of neural networks. In this paper, we discuss two novel approaches for incorporating semantic knowledge into word embeddings. In the first approach, we take advantage of Levy et al’s work which showed that using SVD based methods on co-occurrence matrix provide similar performance to neural network based embeddings. We propose a ‘sprinkling’ technique to add semantic relations to the co-occurrence matrix directly before factorization. In the second approach, WordNet similarity scores are used to improve the retrofitting method. We evaluate the proposed methods in both intrinsic and extrinsic tasks and observe significant improvements over the baselines in many of the datasets.


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Protein Word Detection using Text Segmentation Techniques
Devi Ganesan | Ashish V. Tendulkar | Sutanu Chakraborti
BioNLP 2017

Literature in Molecular Biology is abundant with linguistic metaphors. There have been works in the past that attempt to draw parallels between linguistics and biology, driven by the fundamental premise that proteins have a language of their own. Since word detection is crucial to the decipherment of any unknown language, we attempt to establish a problem mapping from natural language text to protein sequences at the level of words. Towards this end, we explore the use of an unsupervised text segmentation algorithm to the task of extracting “biological words” from protein sequences. In particular, we demonstrate the effectiveness of using domain knowledge to complement data driven approaches in the text segmentation task, as well as in its biological counterpart. We also propose a novel extrinsic evaluation measure for protein words through protein family classification.


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Towards Creating Pedagogic Views from Encyclopedic Resources
Ditty Mathew | Dhivya Eswaran | Sutanu Chakraborti
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications


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Sprinkling Topics for Weakly Supervised Text Classification
Swapnil Hingmire | Sutanu Chakraborti
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Parallels between Linguistics and Biology
Sutanu Chakraborti | Ashish Tendulkar
Proceedings of the 2013 Workshop on Biomedical Natural Language Processing