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Priyansh Singhal

dblp:406/4339 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0002-1202-386XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
question answering
1.012026
Toward a Telugu Question Answering Dataset for Agricultural IR to Serve Marginal Farmers in Drought-Prone Rayalaseema: A Gap Analysis and Roadmap · SIGIR 2026
Information retrieval › evaluation › benchmark dataset
benchmark dataset construction
0.312026
Toward a Telugu Question Answering Dataset for Agricultural IR to Serve Marginal Farmers in Drought-Prone Rayalaseema: A Gap Analysis and Roadmap · SIGIR 2026
Information retrieval
retrieval evaluation
0.312026
Toward a Telugu Question Answering Dataset for Agricultural IR to Serve Marginal Farmers in Drought-Prone Rayalaseema: A Gap Analysis and Roadmap · SIGIR 2026

Methods — techniques the papers use, named apart from their topics

gap analysis · 1.0
YearPublicationVenuePosition
2026 Toward a Telugu Question Answering Dataset for Agricultural IR to Serve Marginal Farmers in Drought-Prone Rayalaseema: A Gap Analysis and Roadmap
abstract
Rayalaseema in southern India is one of the country's most drought-vulnerable regions, experiencing drought in 15 of 18 years between 2000-2018. Over 80% of its 2.7 million farmers are marginal or small landholders practicing rainfed agriculture. Despite pressing agricultural information needs around water management, crop selection, and drought adaptation, these farmers face compounding barriers: 36% rural internet penetration, 60% literacy rates, and information systems designed for English or Hindi speakers. We examine the current landscape of agricultural information retrieval resources for Telugu-speaking farmers and find a critical gap: while agricultural QA datasets exist for Hindi, Tamil, and Bengali, no Telugu agricultural QA benchmark exists, let alone one addressing drought-specific contexts. We document available information sources, identify where they fall short, and propose a methodology for constructing a Telugu Agricultural QA dataset using Kisan Call Center records and regional extension materials. This work charts a path toward inclusive agricultural IR for over 74 million Telugu speakers.
Piyush Joshi, Priyansh Singhal
SIGIR2