Melanie J. Martin

dblp:92/1465 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2004
0009-0001-1125-9910ORCID · reported

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

Databases, data management, data science and information retrieval · 1 · 1 first-author

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 · 56% Data mining · 44%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › clustering
document clustering
0.012004
Reliability and verification of natural language text on the world wide web (abstract only) · SIGIR 2004
Information retrieval › web search
web information retrieval
0.012004
Reliability and verification of natural language text on the world wide web (abstract only) · SIGIR 2004

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

reliability ranking · 0.0query processing · 0.0document retrieval · 0.0
YearPublicationVenuePosition
2004 Reliability and verification of natural language text on the world wide web (abstract only)
abstract
The hypothesis that information on the Web can be verified automatically, with minimal user interaction, will be tested by building and evaluating an interactive system. In this paper, verification is defined as a reasonable determination of the truth or correctness of a statement by examination, research, or comparison with similar text. The system will contain modules for reliability ranking, query processing, document retrieval, and document clustering based on agreement. The query processing and document retrieval components will use standard IR techniques. The reliability module will estimate the likelihood that a statement on the Web can be trusted using standards developed by information scientists, as well as linguistic aspects of the page and the link structure of associated web pages. The clustering module will cluster relevant documents based on whether or not they agree or disagree with the statement to be verified. Relevant references are discussed.
Melanie J. Martin
SIGIR1