InforLorV4, Main, Exploration, bibRecord, 002A57

Memory-Based Active Learning for French Broadcast News

Identifieur interne : 002A57 ( Main/Exploration ); précédent : 002A56; suivant : 002A58

Memory-Based Active Learning for French Broadcast News

Auteurs : Frédéric Tantini [France] ; Christophe Cerisara [France] ; Claire Gardent [France]

Source :

RBID : Hal:inria-00540423

Abstract

Stochastic dependency parsers can achieve very good results when they are trained on large corpora that have been manually annotated. Active learning is a procedure that aims at reducing this annotation cost by selecting as few sentences as possible that will produce the best possible parser. We propose a new selective sampling function for Active Learning that exploits two memory-based distances to find a good compromise between parser uncertainty and sentence representativeness. The reduced dependency between both parsing and selection models opens interesting perspectives for future models combination. The approach is validated on a French broadcast news corpus creation task dedicated to dependency parsing. It outperforms the baseline uncertainty entropy-based selective sampling on this task. We plan to extend this work with self- and co-training methods in order to enlarge this corpus and produce the first French broadcast news Tree Bank.

Url:

https://hal.inria.fr/inria-00540423

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 003153
to stream Hal, to step Curation: 003153
to stream Hal, to step Checkpoint: 002297
to stream Main, to step Merge: 002B21
to stream Main, to step Curation: 002A57

Le document en format XML

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<front><div type="abstract" xml:lang="en">Stochastic dependency parsers can achieve very good results when they are trained on large corpora that have been manually annotated. Active learning is a procedure that aims at reducing this annotation cost by selecting as few sentences as possible that will produce the best possible parser. We propose a new selective sampling function for Active Learning that exploits two memory-based distances to find a good compromise between parser uncertainty and sentence representativeness. The reduced dependency between both parsing and selection models opens interesting perspectives for future models combination. The approach is validated on a French broadcast news corpus creation task dedicated to dependency parsing. It outperforms the baseline uncertainty entropy-based selective sampling on this task. We plan to extend this work with self- and co-training methods in order to enlarge this corpus and produce the first French broadcast news Tree Bank.</div>
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Memory-Based Active Learning for French Broadcast News

Memory-Based Active Learning for French Broadcast News

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