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%2F68407700%3A21240%2F17%3A00314191" target="_blank" >RIV/68407700:21240/17:00314191 - 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

J_ost - Ostatní články v recenzovaných periodicích

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

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

CEUR workshop proceedings

ISSN

1613-0073

e-ISSN

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Svazek periodika

2017

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

9

Strana od-do

144-152

Kód UT WoS článku

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EID výsledku v databázi Scopus

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