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Text & Graph Analytics



The Text and Graph Analytics (TGA) area focuses on research and development of innovative solutions for analyzing large-scale unstructured text and graph data for solving real-life business problems. The sharp rise in unstructured data over the recent years has been attributed to the ease of content generation through various communication channels and steady increase of connections among people, leading to the evolution of large-scale network structures. We see this as an opportunity to solve real-life business problems emerging from Big Text Data and Networks.

The TGA team explores text and graph analytics challenges along verticals such as social media, customer care, healthcare and education. We work on problems which are at the intersection of natural language processing, machine learning, and graph analytics. We apply deep learning, transfer learning, graph modelling, and computational linguistic techniques to novel domains.

Team


Akhil

Manjira

Raghuveer

Sachin

Sandya

Shreshtha

Sriranjani

Featured Publications

  • Shreshtha Mundra, Sandya Mannarswamy, Manjira Sinha, Anirban Sen. Embedding Learning of Figurative Phrases for Emotion Classification in Micro-Blog Texts. CODS 2017.
  • Akhil Arora, Sainyam Galhotra, Sayan Ranu. Debunking the Myths of Influence Maximization. North East Database Day (NEDB), 2017.
  • Anirban Sen, Sandya Mannarswamy, Manjira Sinha. Multi-task Representation Learning for Enhanced Emotion Categorization in Short Text. The Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD 2017
  • Shreshtha Mundra, Manjira Sinha, Sandya Mannarswamy, Anirban Sen. Fine-grained Emotion Detection in Contact Center Chat Utterances. The Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD 2017
  • Akhil Arora, Sainyam Galhotra, Sayan Ranu. Debunking the Myths of Influence Maximization. ACM SIGMOD International Conference on Management of Data, 2017.

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