Search Results for author: Claire Brierley

Found 5 papers, 0 papers with code

An Empirical Study of Arabic Formulaic Sequence Extraction Methods

no code implementations LREC 2016 Ayman Alghamdi, Eric Atwell, Claire Brierley

This paper aims to implement what is referred to as the collocation of the Arabic keywords approach for extracting formulaic sequences (FSs) in the form of high frequency but semantically regular formulas that are not restricted to any syntactic construction or semantic domain.

Compilation of an Arabic Children's Corpus

no code implementations LREC 2016 Latifa Al-Sulaiti, Noorhan Abbas, Claire Brierley, Eric Atwell, Ayman Alghamdi

Inspired by the Oxford Children{'}s Corpus, we have developed a prototype corpus of Arabic texts written and/or selected for children.

General Classification text-classification +1

Tools for Arabic Natural Language Processing: a case study in qalqalah prosody

no code implementations LREC 2014 Claire Brierley, Majdi Sawalha, Eric Atwell

In this paper, we focus on the prosodic effect of qalqalah or {``}vibration{''} applied to a subset of Arabic consonants under certain constraints during correct Qur{'}anic recitation or ta{\c{C}}{\S}w{\=\i}d, using our Boundary-Annotated QurÂ’an dataset of 77430 words (Brierley et al 2012; Sawalha et al 2014).

Keyword Extraction

Predicting Phrase Breaks in Classical and Modern Standard Arabic Text

no code implementations LREC 2012 Majdi Sawalha, Claire Brierley, Eric Atwell

We train and test two probabilistic taggers for Arabic phrase break prediction on a purpose-built, “gold standard”, boundary-annotated and PoS-tagged Qur'an corpus of 77430 words and 8230 sentences.

Chunking Human Parsing +3

Open-Source Boundary-Annotated Corpus for Arabic Speech and Language Processing

no code implementations LREC 2012 Claire Brierley, Majdi Sawalha, Eric Atwell

We take a novel approach to phrase break prediction for Arabic, deriving our prosodic annotation scheme from Tajw{\=\i}d (recitation) mark-up in the Qur'an which we then interpret as additional text-based data for computational analysis.

Chunking Descriptive +2

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