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Elke Mittendorf

dblp:53/4809 · DBLP profile ↗
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5ranked-venue papers
4as first author
0since 2021 · last 2000
—ORCID · none

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

Databases, data management, data science and information retrieval · 5 · 4 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
2 papers
Information retrieval · 82% Data mining · 18%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › indexing › document indexing
OCR-based indexing		 0.011995
Applying Probabilistic Term Weighting to OCR Text in the Case of a Large Alphabetic Library Catalogue · SIGIR 1995
Information retrieval › retrieval models › term weighting
probabilistic term weighting		 0.011995
Applying Probabilistic Term Weighting to OCR Text in the Case of a Large Alphabetic Library Catalogue · SIGIR 1995
Information retrieval
document retrieval		 0.011994
Document and Passage Retrieval Based on Hidden Markov Models · SIGIR 1994
Data mining › probabilistic graphical models
hidden markov model		 0.011994
Document and Passage Retrieval Based on Hidden Markov Models · SIGIR 1994
Information retrieval › document retrieval
passage retrieval		 0.011994
Document and Passage Retrieval Based on Hidden Markov Models · SIGIR 1994
Information retrieval
digital libraries		 0.011995
Applying Probabilistic Term Weighting to OCR Text in the Case of a Large Alphabetic Library Catalogue · SIGIR 1995

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

probabilistic weighting · 0.0optical character recognition · 0.0hidden markov model · 0.0
YearPublicationVenuePosition
2000 Using the Co-occurrence of Words for Retrieval Weighting
Elke Mittendorf, Bojidar Mateev, Peter Schäuble
 Inf. Retr.1
2000 Information Retrieval can Cope with Many Errors
Elke Mittendorf, Peter Schäuble
 Inf. Retr.1
2000 Introduction
Peter Schäuble, Elke Mittendorf
 Inf. Retr.2
1995 Applying Probabilistic Term Weighting to OCR Text in the Case of a Large Alphabetic Library Catalogue
abstract
We report on a probabilistic weighting approach to indexing the scanned images of very short documents.This fully automatic process copes with short and very noisy texts (67% word accuracy)derived from the images by Optical Character Recognition (OCR).The probabilistic term weighting
Elke Mittendorf, Peter Schäuble, Paraic Sheridan
 SIGIR1
1994 Document and Passage Retrieval Based on Hidden Markov Models
Elke Mittendorf, Peter Schäuble
 SIGIR1