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

Helen L. Gaylard

dblp:41/198 · also Helen Seville · DBLP profile ↗
← Back
3ranked-venue papers
1as first author
0since 2021 · last 2003
0000-0002-0322-0821ORCID · reported

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

Artificial intelligence and machine learning · 2 · 1 first-authorDatabases, 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.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Information retrieval › evaluation
retrieval effectiveness		 0.012003
An investigation of broad coverage automatic pronoun resolution for information retrieval · SIGIR 2003
Information retrieval › retrieval models
term weighting		 0.012003
An investigation of broad coverage automatic pronoun resolution for information retrieval · SIGIR 2003
Information retrieval › document processing › document analysis
document representation		 0.012003
An investigation of broad coverage automatic pronoun resolution for information retrieval · SIGIR 2003
Information retrieval
term frequency		 0.012003
An investigation of broad coverage automatic pronoun resolution for information retrieval · SIGIR 2003

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

automatic pronoun resolution · 0.0
YearPublicationVenuePosition
2003 An investigation of broad coverage automatic pronoun resolution for information retrieval
abstract
Term weighting methods have been shown to give significant increases in information retrieval performance. The presence of pronomial references in documents reduces the term frequencies of associated words with a consequent effect on term weights and information retrieval behaviour. This investigation explores the impact on information retrieval performance of broad coverage automatic pronoun resolution. Results indicate that this approach has potential to improve both precision at fixed cutoff levels and average precision.
Richard J. Edens, Helen L. Gaylard, Gareth J. F. Jones, Adenike M. Lam-Adesina
 SIGIR2
2000 Unscrambling English word order
Allan Ramsay, Helen L. Gaylard
 COLING2
2000 Making Sense of Reference to the Unfamiliar?
Helen L. Gaylard, Allan Ramsay
 COLING1