Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDeep Learning of Inflection and the Cell-Filling Problem
Anno di pubblicazione2018
Autore/iCardillo, F.A.; Ferro, M.; Marzi, C.; Pirrelli, V.
Affiliazioni autoriILC-CNR; ILC-CNR; ILC-CNR; ILC-CNR;
Autori CNR e affiliazioni
  • inglese
AbstractMachine learning offers two basic strategies for morphology induction: lexical segmentation and surface word relation. The first approach assumes that words can be segmented into morphemes. Inferring a novel inflected form requires identification of morphemic constituents and a strategy for their recombination. The second approach dispenses with segmentation: lexical representations form part of a network of associatively related inflected forms. Production of a novel form consists in filling in one empty node in the network. Here, we present the results of a task of word inflection by a recurrent LSTM network that learns to fill in paradigm cells of incomplete verb paradigms. Although the task does not require morpheme segmentation, we show that accuracy in carrying out the inflection task is a function of the model's sensitivity to paradigm distribution and morphological structure.
Lingua abstractinglese
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Pagine da57
Pagine a75
Pagine totali21
RivistaItalian Journal of Computational Linguistics
Paese di pubblicazione: Italia
ISSN: 2499-4553
Titolo chiave: Italian Journal of Computational Linguistics
Numero volume della rivista4
Fascicolo della rivista1
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveDeep Learning, LSTM, Cell-Filling Problem
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Strutture CNR
  • ILC — Istituto di linguistica computazionale "Antonio Zampolli"
Moduli CNR
    Progetti Europei-
    • Deep Learning of Inflection and the Cell-Filling Problem
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