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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F17%3A00314191" target="_blank" >RIV/68407700:21240/17:00314191 - 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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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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Type

J_ost - Miscellaneous article in a specialist periodical

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)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Name of the periodical

CEUR workshop proceedings

ISSN

1613-0073

e-ISSN

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Volume of the periodical

2017

Issue of the periodical within the volume

9

Country of publishing house

DE - GERMANY

Number of pages

9

Pages from-to

144-152

UT code for WoS article

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EID of the result in the Scopus database

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