Learning agglutinative morphology of indian languages with linguistically motivated adaptor grammars
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/83344
Tipus de documentText en actes de congrés
Data publicació2016
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
In this paper an automatic morphology learning system for complex and agglutinative languages is presented. We process complex agglutinative morphology of Indian languages using Adaptor Grammars and linguistic rules of morphology. Adaptor Grammars are a compositional Bayesian framework for grammatical inference, where we define a morphological boundaries are inferred from a corpora of plain text. Once it produces morphological segmentation, regular expressions for orthography rules are applied to achieve final segmentation. We test our algorithm in the case of three complex languages from the Dravidian family and evaluate the results comparing to other state of the art unsupervised morphology learning systems and show significant improvements in the results.
CitacióKumar, A., Padro, L., Oliver, A. Learning agglutinative morphology of indian languages with linguistically motivated adaptor grammars. A: Recent Advances in Natural Language Processing. "RANLP 2015: International Conference on Recent Advances in Natural Language Processing: Hissar, Bulgaria: September 7-9, 2015: proceedings book". Hissar: 2016, p. 307-312.
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
kumar15b.pdf | 4,953Mb | Visualitza/Obre |