no code implementations • 31 Oct 2023 • Santosh Palaskar, Vijay Ekambaram, Arindam Jati, Neelamadhav Gantayat, Avirup Saha, Seema Nagar, Nam H. Nguyen, Pankaj Dayama, Renuka Sindhgatta, Prateeti Mohapatra, Harshit Kumar, Jayant Kalagnanam, Nandyala Hemachandra, Narayan Rangaraj
Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data.
no code implementations • 2 Jan 2020 • Chandresh Kumar Maurya, Neelamadhav Gantayat, Sampath Dechu, Tomas Horvath
In this paper, we present two approaches to solve the above problem using various types of available agent's recorded feedback data.
no code implementations • 9 Aug 2019 • Prateeti Mohapatra, Neelamadhav Gantayat, Gargi B. Dasgupta
In this paper, we investigate the integration of sentence position and semantic role of words in a PageRank system to build a key phrase ranking method.
no code implementations • EMNLP 2018 • Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.
no code implementations • 1 Jan 2018 • Rahul Aralikatte, Neelamadhav Gantayat, Naveen Panwar, Anush Sankaran, Senthil Mani
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.
no code implementations • 2 Nov 2017 • Senthil Mani, Neelamadhav Gantayat, Rahul Aralikatte, Monika Gupta, Sampath Dechu, Anush Sankaran, Shreya Khare, Barry Mitchell, Hemamalini Subramanian, Hema Venkatarangan
Question answering is one of the primary challenges of natural language understanding.
no code implementations • 16 Aug 2017 • Naveen Panwar, Shreya Khare, Neelamadhav Gantayat, Rahul Aralikatte, Senthil Mani, Anush Sankaran
Cross-modal data retrieval has been the basis of various creative tasks performed by Artificial Intelligence (AI).
no code implementations • 16 Aug 2017 • Rahul Aralikatte, Giriprasad Sridhara, Neelamadhav Gantayat, Senthil Mani
Further, we developed three systems; two of which were based on traditional machine learning and one on deep learning to automatically identify reviews whose rating did not match with the opinion expressed in the review.