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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

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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

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Academic Paper


Title: Estimating the number of segments for improving dialogue act labelling
Author: Vicent Tamarit
Institution: Universidad Politécnica de Valencia
Author: Carlos-D. Martínez-Hinarejos
Institution: Universidad Politécnica de Valencia
Author: José-Miguel Benedí
Institution: Universidad Politécnica de Valencia
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: In dialogue systems it is important to label the dialogue turns with dialogue-related meaning. Each turn is usually divided into segments and these segments are labelled with dialogue acts (DAs). A DA is a representation of the functional role of the segment. Each segment is labelled with one DA, representing its role in the ongoing discourse. The sequence of DAs given a dialogue turn is used by the dialogue manager to understand the turn. Probabilistic models that perform DA labelling can be used on segmented or unsegmented turns. The last option is more likely for a practical dialogue system, but it provides poorer results. In that case, a hypothesis for the number of segments can be provided to improve the results. We propose some methods to estimate the probability of the number of segments based on the transcription of the turn. The new labelling model includes the estimation of the probability of the number of segments in the turn. We tested this new approach with two different dialogue corpora: Switchboard and Dihana . The results show that this inclusion significantly improves the labelling accuracy.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 18, Issue 1, which you can read on Cambridge's site .



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