Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Olga Ourioupina

dblp:213/3469 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2002
—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%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 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 › corpus linguistics
corpus statistics		 0.012002
Using the Web to Overcome Data Sparseness · EMNLP 2002
Information retrieval
search engines		 0.012002
Using the Web to Overcome Data Sparseness · EMNLP 2002
Information retrieval › query processing
web query processing		 0.012002
Using the Web to Overcome Data Sparseness · EMNLP 2002

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

web search querying · 0.1plausibility judgment · 0.1corpus frequency comparison · 0.1
YearPublicationVenuePosition
2002 Using the Web to Overcome Data Sparseness
abstract
This paper shows that the web can be employed to obtain frequencies for bigrams that are unseen in a given corpus. We describe a method for retrieving counts for adjective-noun, noun-noun, and verb-object bigrams from the web by querying a search engine. We evaluate this method by demonstrating that web frequencies and correlate with frequencies obtained from a carefully edited, balanced corpus. We also perform a task-based evaluation, showing that web frequencies can reliably predict human plausibility judgments.
Frank Keller, Maria Lapata, Olga Ourioupina
 EMNLP3