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Narayan Acharya

dblp:278/8056 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1

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

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval › domain-specific retrieval
biomedical information retrieval
0.412020
Querying Across Genres for Medical Claims in News · EMNLP (1) 2020
Information retrieval › fact-checking
claim verification
0.412020
Querying Across Genres for Medical Claims in News · EMNLP (1) 2020

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

transformer · 0.4BM25 · 0.4
YearPublicationVenuePosition
2020 Querying Across Genres for Medical Claims in News
abstract
We present a query-based biomedical information retrieval task across two vastly different genres -newswire and research literaturewhere the goal is to find the research publication that supports the primary claim made in a health-related news article.For this task, we present a new dataset of 5,034 claims from news paired with research abstracts.Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list.We compare the classical IR approach using BM25 with more recent transformerbased models.Our results show that crossgenre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.
Chaoyuan Zuo, Narayan Acharya, Ritwik Banerjee
EMNLP (1)2