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EDBT 2026 Demo / reviewers in the wild / expert

Aman Saini

dblp:277/2235 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Artificial intelligence and machine learning · 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.

Artificial intelligence
1 paper
Information extraction and text analysis · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 77% Web and social media mining · 23%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
entity linking		 0.612022
TweetNERD - End to End Entity Linking Benchmark for Tweets · NeurIPS 2022
Natural language and speech › Information extraction and text analysis › named entity processing
named entity recognition and disambiguation		 0.612022
TweetNERD - End to End Entity Linking Benchmark for Tweets · NeurIPS 2022
Web and social media mining
social media text		 0.212022
TweetNERD - End to End Entity Linking Benchmark for Tweets · NeurIPS 2022
YearPublicationVenuePosition
2022 TweetNERD - End to End Entity Linking Benchmark for Tweets
abstract
Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on Tweets. This is the largest and most temporally diverse open sourced dataset benchmark for NERD on Tweets and can be used to facilitate research in this area. We describe evaluation setup with TweetNERD for three NERD tasks: Named Entity Recognition (NER), Entity Linking with True Spans (EL), and End to End Entity Linking (End2End); and provide performance of existing publicly available methods on specific TweetNERD splits. TweetNERD is available at: https://doi.org/10.5281/zenodo.6617192 under Creative Commons Attribution 4.0 International (CC BY 4.0) license. Check out more details at https://github.com/twitter-research/TweetNERD.
Shubhanshu Mishra, Aman Saini, Raheleh Makki, Sneha Mehta, Aria Haghighi, Ali Mollahosseini
 NeurIPS2