By Juan Diego Quesada

ISBN-10: 3895866717

ISBN-13: 9783895866715

This can be the 1st accomplished description of the grammatical constitution of Teribe, a language of Panama, and a member of the Chibchan kin of languages, which covers a large region starting from Northeastern Honduras, in the course of the Atlantic of Nicaragua, so much of Costa Rica, Panama, Colombia, to the West of Venezuela. Spoken by way of a few 1,000 humans, Teribe is at the moment very likely, notwithstanding now not critically, endangered. This grammar fills a lacuna in a twofold demeanour: it offers an intensive account of the constitution of Teribe, and in doing so, it reduces the variety of crucial American Chibchan languages being undescribed. This grammar is hence meant as a contribution to the outline of the language at 3 degrees: a. language-immanent; b. Chibchan comparative grammar; c. documentation of endangered languages, specially these of the yank continent. Teribe is either a transparent consultant of the "Central American" positive aspects of the Chibchan languages (numeral classifiers, constrained contract, no evidentials), and idiosyncratic inside those languages (having such phenomena as an inverse development and verb serialization). The examine for this publication was once funded in its entirety by way of the Social Sciences and arts examine Council of Canada.

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5: Supervised and unsupervised model comparison; tested on English-Hebrew NE pairs. Learning curves for Hebrew under two different settings: unsupervised (UCD) and supervised (denoted SCD). Goldwasser & Roth (2008a) model is also shown (GR’08a). Our unsupervised model outperforms the supervised model, trained on 250 labeled examples of our framework over the English-Hebrew dataset. Interestingly, the unsupervised system outperforms the supervised version of the system. This can be explained by the fact that the unsupervised system uses the testing data as training data, allowing it to better adapt to the specific classification instances as it iterates over that data.

CHANG, Y. TU & D. ROTH Settings Roman. table Roman. table +learn. +Gen Const. +Gen Const. +learn. +All Const. +All Const. +learn. +All Const. +learn. +All Const. +learn. unig. unig. unig. unig. unig. unig. big. ) big. 3) Table 2: Results of an ablation study of the unsupervised method for three target languages. 4 Ablation study Our system combines several resources and exploits several different intuitions about the transliteration domain. The resources used in our framework consist of a Romanization table and language specific transliteration constraints; in addition our system encodes the dependency between feature activations as general constraints, and it can make use of character unigrams features only, or both character unigrams and bigrams features.

Our full model uses both unigram and bigram features. However, the supervision signal, obtained from the Romanization table, is limited to unigram features alone. To provide the unsupervised model with a better starting point, the system was trained in two stages – first, using only unigram features, initialized using the Romanization table, and once the model converged we added the bigram features, initialized with a weight of 0. Experiments showed that this training protocol resulted in a considerable improvement.

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A grammar of Teribe by Juan Diego Quesada


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