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The Social Origins of Language

By Daniel Dor

Presents a new theoretical framework for the origins of human language and sets key issues in language evolution in their wider context within biological and cultural evolution


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Preposition Placement in English: A Usage-Based Approach

By Thomas Hoffmann

This is the first study that empirically investigates preposition placement across all clause types. The study compares first-language (British English) and second-language (Kenyan English) data and will therefore appeal to readers interested in world Englishes. Over 100 authentic corpus examples are discussed in the text, which will appeal to those who want to see 'real data'


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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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