K-best Viterbi Semi-supervized Active Learning in Sequence Labelling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00478628" target="_blank" >RIV/67985807:_____/17:00478628 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-1885/144.pdf" target="_blank" >http://ceur-ws.org/Vol-1885/144.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
K-best Viterbi Semi-supervized Active Learning in Sequence Labelling
Original language description
In application domains where there exists a large amount of unlabelled data but obtaining labels is expensive, active learning is a useful way to select which data should be labelled. In addition to its traditional successful use in classification and regression tasks, active learning has been also applied to sequence labelling. According to the standard active learning approach, sequences for which the labelling would be the most informative should be labelled. However, labelling the entire sequence may be inefficient as for some its parts, the labels can be predicted using a model. Labelling such parts brings only a little new information. Therefore in this paper, we investigate a sequence labelling approach in which in the sequence selected for labelling, the labels of most tokens are predicted by a model and only tokens that the model can not predict with sufficient confidence are labelled. Those tokens are identified using the k-best Viterbi algorithm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings ITAT 2017: Information Technologies - Applications and Theory
ISBN
978-1974274741
ISSN
1613-0073
e-ISSN
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Number of pages
9
Pages from-to
144-152
Publisher name
Technical University & CreateSpace Independent Publishing Platform
Place of publication
Aachen & Charleston
Event location
Martinské hole
Event date
Sep 22, 2017
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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