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VLDB 2026 Research / reviewers in the wild / expert

Paul Aarseth

dblp:23/3115 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—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.

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

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › semantic role labeling
predicate-argument structure		 0.012003
Using Predicate-Argument Structures for Information Extraction · ACL 2003
Natural language and speech › Information extraction and text analysis
relation extraction		 0.012003
Using Predicate-Argument Structures for Information Extraction · ACL 2003

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

feature engineering · 0.0decision tree learning · 0.0
YearPublicationVenuePosition
2003 Using Predicate-Argument Structures for Information Extraction
abstract
In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures.We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm.It is based on: (1) an extended set of features; and (2) inductive decision tree learning.The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results.
Mihai Surdeanu, Sanda M. Harabagiu, John Williams 0001, Paul Aarseth
 ACL4