Počet záznamů: 1
K-best Viterbi Semi-supervized Active Learning in Sequence Labelling
- 1.
SYSNO ASEP 0478628 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název K-best Viterbi Semi-supervized Active Learning in Sequence Labelling Tvůrce(i) Šabata, T. (CZ)
Borovička, T. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings ITAT 2017: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2017 / Hlaváčová J. - ISSN 1613-0073 - ISBN 978-1974274741 Rozsah stran s. 144-152 Poč.str. 9 s. Forma vydání Online - E Akce ITAT 2017. Conference on Theory and Practice of Information Technologies - Applications and Theory /17./ Datum konání 22.09.2017 - 26.09.2017 Místo konání Martinské hole Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova active learning ; semi-supervised learning ; sequence labelling ; Viterbi algorithm Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85045738626 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2018 Elektronická adresa http://ceur-ws.org/Vol-1885/144.pdf
Počet záznamů: 1