Output details
11 - Computer Science and Informatics
University of Oxford
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Output 0 of 0 in the submission
Article title
Aggregating Semantic Annotators
Type
D - Journal article
DOI
-
Title of journal
The VLDB Journal
Article number
-
Volume number
6
Issue number
13
First page of article
1486
ISSN of journal
2150-8097
Year of publication
2013
URL
-
Number of additional authors
3
Additional information
<12>
This paper presents highly-scalable methods for reconciling independent Named Entity Extractors (NER). The paper has particular significance since it provides for the first time an extensive and insightful analysis of the performance of state-of-the art NERs showing the need for integration and conflict resolution. The paper introduces two highly-scalable aggregation algorithms that consistently outperform existing NER systems and state-of-the-art aggregators both in coverage and accuracy. VLDB has been the reference conference for the Database community for more than 30 years with an average acceptance rate of 17% in the last 10 years.
Interdisciplinary
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Cross-referral requested
-
Research group
None
Citation count
-
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
