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Oxford Handbook of Corpus Phonology

Edited by Jacques Durand, Ulrike Gut, and Gjert Kristoffersen

Offers the first detailed examination of corpus phonology and serves as a practical guide for researchers interested in compiling or using phonological corpora


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The Languages of the Jews: A Sociolinguistic History

By Bernard Spolsky

A vivid commentary on Jewish survival and Jewish speech communities that will be enjoyed by the general reader, and is essential reading for students and researchers interested in the study of Middle Eastern languages, Jewish studies, and sociolinguistics.


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Indo-European Linguistics

New Open Access journal on Indo-European Linguistics is now available!


Academic Paper


Title: Linguistic Knowledge in Statistical Phrase-based Word Alignment
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: In this paper, a novel phrase alignment strategy combining linguistic knowledge and co-occurrence measures extracted from bilingual corpora is presented. The algorithm is mainly divided into four steps, namely phrase selection and classification, phrase alignment, one-to-one word alignment and post-processing. The first stage selects a linguistically-derived set of phrases that convey a unified meaning during translation and are therefore aligned together in parallel texts. These phrases include verb phrases, idiomatic expressions and date expressions. During the second stage, very high precision links between these selected phrases for both languages are produced. The third step performs a statistical word alignment using association measures and link probabilities with the remaining unaligned tokens, and finally the fourth stage takes final decisions on unaligned tokens based on linguistic knowledge. Experiments are reported for an English-Spanish parallel corpus, with a detailed description of the evaluation measure and manual reference used. Results show that phrase co-occurrence measures convey a complementary information to word co-occurrences and a stronger evidence of a correct alignment, successfully introducing linguistic knowledge in a statistical word alignment scheme. Precision, Recall and Alignment Error Rate (AER) results are presented, outperforming state-of-the-art alignment algorithms.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 12, Issue 1, which you can read on Cambridge's site or on LINGUIST .



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