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dc.date.accessioned2020-03-26T13:33:58Z-
dc.date.available2020-03-26T13:33:58Z-
dc.date.issued2019-
dc.identifier.citationZammit, A. (2019). A dependency parser for the Maltese language using deep neural networks (Master's dissertation).en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/53162-
dc.descriptionM.SC.ARTIFICIAL INTELLIGENCEen_GB
dc.description.abstractTasks such as information retrieval, sentiment analysis and question answering require the processing of text analysis and natural language processing. Sentence parsing is one of the tasks performed in NLP to analyse the grammar structure of a sentence, with the aim of determining the relationships between the words in a sentence. Whilst there are several parsers for many European languages, Maltese remains a lowresourced and low-researched language and currently there are no parsers for the Maltese language. This work investigates computational parsing of Maltese by using novel Deep Learning and source bootstrapping techniques, with the aim of contributing not only to the increase in computational resources for Maltese, but also to dependency parsing. The evaluation of the parser was performed according to the Conference on Computational Natural Language Learning (CoNLL) standards and metrics. Experiments were conducted using datasets provided during CoNLL 2017 except for the Maltese language dataset which is provided directly by the author. Results show an Unlabelled Attachment Score of 90% and Labelled Attachment Score of 86% by using a Quasi-Recurrent Neural Network (QRNN) with a bootstrapped data source ofMalteseandotherRomancelanguages. Bi-directionalLSTMNeuralNetworksoutperform QRNN by less than 0.2% in both metrics however, QRNN achieve a three-fold runtime performance over bi-LSTM. To our knowledge, this is the first time that QRNN is applied to the task of dependency parsing. The use of bootstrapped data sources is not documented in the published papers and proceedings of the 2017 shared task we reviewed.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectParsing (Computer grammar)en_GB
dc.subjectNeural networks (Computer science)en_GB
dc.subjectNatural language processing (Computer science)en_GB
dc.subjectMaltese languageen_GB
dc.titleA dependency parser for the Maltese language using deep neural networksen_GB
dc.typemasterThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technology. Department of Artificial Intelligenceen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorZammit, Andrei-
Appears in Collections:Dissertations - FacICT - 2019
Dissertations - FacICTAI - 2019

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