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Evolutionary Syntax

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This book "outlines novel and testable hypotheses, contains extensive examples from many different languages" and is "presented in accessible language, with more technical discussion in footnotes."

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Academic Paper

Title: Learning Morphology with Morfette
Paper URL:
Author: Grzegorz ChrupaƂa
Email: click here TO access email
Institution: Saarland University
Author: Georgiana Dinu
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Institution: Saarland University
Author: Josef Van Genabith
Email: click here TO access email
Institution: Dublin City University
Linguistic Field: Computational Linguistics
Abstract: Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the predictions of the Maximum-Entropy models and outputs a probability distribution over tag-lemma pair sequences. The lemmatization module exploits the idea of recasting lemmatization as a classification task by using class labels which encode mappings from word forms to lemmas. Experimental evaluation results and error analysis on three morphologically rich languages show that the system achieves high accuracy with no language-specific feature engineering or additional resources.
Type: Individual Paper
Status: Completed
Venue: LREC 2008

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