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Demo Data from EDBT 2026 / reviewers in the wild / expert

Kfir Zigdon

dblp:74/6566 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

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

Artificial intelligence and machine learning · 1Security and privacy · 1Databases, data management, data science and information retrieval · 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.

Artificial intelligence
1 paper
Information extraction and text analysis · 100%

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 › text mining
authorship analysis		 0.112005
Determining an author's native language by mining a text for errors · KDD 2005
Natural language and speech › Information extraction and text analysis › text mining › authorship analysis
native language identification		 0.112005
Determining an author's native language by mining a text for errors · KDD 2005
Natural language and speech › Information extraction and text analysis › text mining
stylometry		 0.112005
Determining an author's native language by mining a text for errors · KDD 2005

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

support vector machine · 0.1stylistic feature extraction · 0.1
YearPublicationVenuePosition
2005 Automatically Determining an Anonymous Author's Native Language
Moshe Koppel, Jonathan Schler, Kfir Zigdon
 ISI3
2005 Determining an author's native language by mining a text for errors
abstract
In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy. Specifically, we first use automatic tools to ascertain frequencies of various stylistic idiosyncrasies in a text. These frequencies then serve as features for support vector machines that learn to classify texts according to author native language.
Moshe Koppel, Jonathan Schler, Kfir Zigdon
 KDD3