K-best Viterbi Semi-supervized Active Learning in Sequence Labelling

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

K-best Viterbi Semi-supervized Active Learning in Sequence Labelling

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

K-best Viterbi Semi-supervized Active Learning in Sequence Labelling

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-01251S" target="_blank" >GA17-01251S: Metaučení pro extrakci pravidel s numerickými konsekventy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings ITAT 2017: Information Technologies - Applications and Theory

ISBN

978-1974274741

ISSN

1613-0073

e-ISSN

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Počet stran výsledku

9

Strana od-do

144-152

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen & Charleston

Místo konání akce

Martinské hole

Datum konání akce

22. 9. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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