External Lexical Information for Multilingual Part-of-Speech Tagging - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Rapport (Rapport De Recherche) Année : 2016

External Lexical Information for Multilingual Part-of-Speech Tagging

Résumé

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet better performances are obtained with our feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features. This shows that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods.
Fichier principal
Vignette du fichier
RR-8924.pdf (992.79 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01330301 , version 1 (10-06-2016)
hal-01330301 , version 2 (10-06-2016)
hal-01330301 , version 3 (06-08-2016)

Identifiants

  • HAL Id : hal-01330301 , version 1

Citer

Benoît Sagot. External Lexical Information for Multilingual Part-of-Speech Tagging. [Research Report] RR-8924, Inria Paris. 2016. ⟨hal-01330301v1⟩
213 Consultations
330 Téléchargements

Partager

Gmail Facebook X LinkedIn More