S
Sameep Mehta
Researcher at IBM
Publications - 167
Citations - 2826
Sameep Mehta is an academic researcher from IBM. The author has contributed to research in topics: Context (language use) & Service (business). The author has an hindex of 22, co-authored 160 publications receiving 2093 citations. Previous affiliations of Sameep Mehta include Lady Hardinge Medical College & All India Institute of Medical Sciences.
Papers
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Proceedings ArticleDOI
Efficiently Processing Temporal Queries on Hyperledger Fabric
Himanshu Gupta,Sandeep Hans,Kushagra Aggarwal,Sameep Mehta,Bapi Chatterjee,Praveen Jayachandran +5 more
TL;DR: This paper presents two models for overcoming limitations and improving the performance of temporal queries on Fabric, a popular implementation of Blockchain technology and shows that these two models significantly outperform the naive ways of handling temporal query on Fabric.
Journal ArticleDOI
Leveraging semantic resources in diversified query expansion
TL;DR: This paper develops two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively, and shows that SLR performs state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversification query expansion.
Proceedings ArticleDOI
Exploring a Scalable Solution to Identifying Events in Noisy Twitter Streams
TL;DR: This paper investigates event detection in the context of real-time Twitter streams as observed in real-world crises, and presents a novel approach to address the key challenges: the informal nature of text, and the high-volume and high-velocity characteristics of Twitter streams.
Book ChapterDOI
Discovery and analysis of evolving topical social discussions on unstructured microblogs
TL;DR: This work explores semantic, social and temporal relationships of topical clusters formed in Twitter to identify conversations and devise an algorithm comprising of a sequence of steps such as text clustering, topical similarity detection using TF-IDF and Wordnet, and intersecting social, semantic and temporal graphs to discover social conversations around topics.
Proceedings ArticleDOI
Data Quality for Machine Learning Tasks
Nitin Gupta,Shashank Mujumdar,Hima Patel,Satoshi Masuda,Naveen Panwar,Sambaran Bandyopadhyay,Sameep Mehta,Shanmukha C. Guttula,Shazia Afzal,Ruhi Sharma Mittal,Vitobha Munigala +10 more
TL;DR: Data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue as discussed by the authors.