Alghamdi, AAO

Atwell, E

Brierley, C

White Rose Research Online

This is a repository copy of A computational formulaic sequences lexicon for language pedagogy and technology.White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/104802/Version: Published VersionConference or Workshop Item:Alghamdi, AAO, Atwell, E orcid.org/0000-0001-9395-3764 and Brierley, C (2016) A computational formulaic sequences lexicon for language pedagogy and technology. In: UNSPECIFIED. (Unpublished) eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Figure 3: Diagram of the proposed hybrid framework for extracting ArFSs itemsAyman Alghamdi Eric Atwell Claire Brierleyscaaa@leeds.ac.uk e.s.atwell@leeds.ac.uk c.brierley@leeds.ac.ukFigure 2: Machine translation example before and after integrating English formulaic sequences knowledgeFigure 1: Tips of phraseological iceberg showsthe underlying complexity of phrases related to the Arabic word ラΑハMethodology This research aims to adopt a comprehensive hybridapproach for ArFSs extraction; this will be based onthe integration of frequency-based and phraseologicalapproaches, and the combination of knowledge-basedand data-driven approaches to identifying ArFSs.Research Significance The importance of this research is due to a set of factorsrelated to the vital role of integrating the formulaiclanguage knowledge in NLP and language pedagogy. Theignorance of handling MWEs in any language-relatedtasks will have a negative impact on their final outputquality. This is due to the fact that MWEs constitute alarge part of everyday language; for instance, in EnglishMWEs constitute 41% of the entries in WordNet 1.7(Fellbaum, 1998). Li et al. (2003) also stated that phrasalverbs constitute approximately one third of the Englishverb vocabulary. However, this large portion of MWEsemphasises their key role in the development of language-related applications. Figure 2 shows an example illustratesthe differences between machine translation output beforeand after integrating English MWEs knowledge.Formulaic language research provides evidence that themost frequently used words in languages are only the tipof expressional icebergs (e.g., Sinclair, 1987; Martinez &Murphy, 2011). Figure 1 shows the underlying complexityof phrases related to the Arabic word ラΑハ雇ayn ‘Eye’.IntroductionThe phenomenon of formulaic sequences or multi-wordexpressions (MWEs) in languages has attracted the attentionof researchers in various language-related disciplines (e.g.,linguistics, psychology, language pedagogy and naturallanguage processing ‘NLP’). Hence, this phenomenon hasbeen researched from a number of different scientific angles.A considerable amount of research has emphasised the majorrole of MWEs in the process of analysing and understandinglanguages. From the applied linguistic perspective, manystudies have emphasised the crucial importance of includingformulaic language and MWEs in second language learningand teaching. Several researchers have highlighted the factthat the mental lexicon is not merely represented by singleorthographic words, but rather it incorporates longerformulaic sequences (e.g., Pawley & Syder, 1983; Wray,2002; Nesselhauf, 2005). Other researchers have attempted todevelop different MWEs lists, which can be used as apedagogical tool in language teaching and learning (e.g.,material design, curriculum developments and languagetesting). On the other hand, from the computationalperspective, MWEs play a vital role in NLP and manyresearchers have attempted to construct various types ofMWEs repositories in order to apply them in thedevelopment of various NLP applications, which include, butare not limited to, MWE identification and extraction,language parsing, information retrieval and machinetranslation, for example.Motivation The vast majority of research in this area has been applied tothe English language because of the rich availability of freeaccess machine readable language resources. Recently,Arabic is another language that has received more attentionfrom researchers from different, albeit related, disciplines.However, in comparison to English, Arabic MWE research isstill at an early stage. Therefore, the key role of formulaiclanguage and MWE resources in language pedagogy andNLP and the lack of free access to Arabic MWE lexicalresources, justify the conduct of this research to contribute tothe remedying of this deficiency by constructing an Arabiccorpus-informed FSs lexicon for language pedagogy andtechnology.Expected ResultsThe research results are estimated to achieve thefollowing research objectives:- To develop a comprehensive computational corpus-informed ArFSs lexicon, which can be incorporatedinto various Arabic NLP applications.- To establish standards for describing and encodingArFSs lexical entries at different linguistic levels(morphological, syntactic, lexical and semantic).- To propose an overall model for ArFSs identificationand extraction that will best suit the main objectives ofthis research.Research Implications  The pedagogical implications of this lexicon areestimated to facilitate the inclusion of ArFSs in theprocess of learning and teaching Arabic, particularlyfor non-native speakers. The computationalimplications are related to the key role of the ArFSs, asa novel lexical resource, in the improvement of variousArabic NLP tasks and applications. The final novelArFSs lexicon can be integrated into a free accessonline e-language learning environment to make themost out of it.A raw ArabiccorpusPre-processing  phase 1- Statistical phase:- Primitive list of n-grams.- Applying quantitative criteria.- Ranking the ArFSs in descending order according to statistical data.3- Linguistic phase:                                                         -Applying POS tagger to the list.-Linguistic specifications and classifications of ArFSs.- Extracting a corpus-informed examples of ArFSs.2-Qualitative phase:- Applying qualitative criteria.- Experts’ judgment and evaluation of extracted ArFSs.-Validation of criteria.Primitive list of statistically ranked ArFSsPrimitive list of linguistically classified ArFSsPrimitive validated list of ArFSsFirst draft listing of ArFSsFinal revisions and refinementsFinal list of validated ArFSsMachine Translation Output This is a delicious hot dog.This is a delicious hot dog.Before After.>ذا كلب ساخن لذDذha商ダ kalb sダ省n la商┆商Translated as: ‘This is a delicious hot dog’(An animal)..شطرة سجق لذDذة>ذ: ha商h ša職┆ra sajq la商┆商aTranslated as: ‘This is a delicious hot dog’)A food ‘sausage sandwich’)Thank you for taking the time to read my poster, anycomments, feedback or suggestions on this researchwould be highly welcomed and appreciated.www.aymanalghamdi.comTherefore, the selection of formulaic sequences in thislexicon will be based on several pedagogically andNLP relevant criteria from the perspective of secondlanguage comprehension and computationallinguistics. The research will take advantage of thelarge, available and free access Arabic corpora thatincludes written and spoken Modern Standard ArabicFigure 3 shows the adopted model for ArFSsextraction in the first research experiment.References:Pawley, A., & Syder, F. H. 1983. Two puzzles for linguistic theory: Nativelike selection and nativelike fluency. In J.C. Richards & R. W. Schmidt (Eds.), Language and communication (pp. 191-226). New York: Longman.Wray, A. 2002. Formulaic language and the lexicon. Cambridge University Press Cambridge.Martinez, R. and Murphy, V. 2011. Effect of frequency and idiomaticity on second language readingcomprehension. TESOL Quarterly. 45(2), pp.267-290.Li, W. et al. 2003. An expert lexicon approach to identifying English phrasal verbs. In: Proceedings of the 41stAnnual Meeting on Association for Computational Linguistics-Volume 1: Association for ComputationalLinguistics, pp.513-520.Sinclair, J. 1987. Looking up: an account of the COBUILD Project in lexical computing and the development of theCollins COBUILD English language dictionary. London: Collins ELT.Fellbaum, C. 1998. WordNet. Blackwell Publishing Ltd.Nesselhauf, N. 2005. Collocations in a learner corpus. Amsterdam: John Benjamins.

A computational formulaic sequences lexicon for language pedagogy and technology

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A computational formulaic sequences lexicon for language pedagogy and technology

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