Automatic Amharic Part of Speech Tagging (AAPOST) : A Comparative Approach Using Bidirectional LSTM and Conditional Random Fields (CRF) Methods

Lade...
Vorschaubild
Dateien
Birhanie_2-13uilhq9epned7.pdf
Birhanie_2-13uilhq9epned7.pdfGröße: 258.54 KBDownloads: 188
Datum
2020
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Sammlungen
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
GABBIYE HABTU, Nigus, ed., Delele Worku AYELE, ed., Solomon Workneh FANTA, ed. and others. Advances of Science and Techology : 7th EAI International Conference, ICAST 2019, Proceedings. Cham: Springer, 2020, pp. 512-521. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST). 308. ISSN 1867-8211. eISSN 1867-822X. ISBN 978-3-030-43689-6. Available under: doi: 10.1007/978-3-030-43690-2_37
Zusammenfassung

Part of speech (POS) tagging is an initial task for many natural language applications. POS tagging for Amharic is in its infancy. This study contributes towards the improvement of Amharic POS tagging by experimenting using Deep Learning and Conditional Random Fields (CRF) approaches. Word embedding is integrated into the system to enhance performance. The model was applied to an Amharic news corpus tagged into 11 major part of speeches and achieved accuracies of 91.12% and 90% for the Bidirectional LSTM and CRF methods respectively. The result shows that the Bidirectional LSTM approach performance is better than the CRF method. More enhancement is expected in the future by increasing the size and diversity of Amharic corpus.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
400 Sprachwissenschaft, Linguistik
Schlagwörter
Amharic, POS, BI-LSTM, CRF
Konferenz
Advances of Science and Technology : 7th EAI International Conference, ICAST 2019, 2. Aug. 2019 - 4. Aug. 2019, Bahir Dar, Ethiopia
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690BIRHANIE, Worku Kelemework, Miriam BUTT, 2020. Automatic Amharic Part of Speech Tagging (AAPOST) : A Comparative Approach Using Bidirectional LSTM and Conditional Random Fields (CRF) Methods. Advances of Science and Technology : 7th EAI International Conference, ICAST 2019. Bahir Dar, Ethiopia, 2. Aug. 2019 - 4. Aug. 2019. In: GABBIYE HABTU, Nigus, ed., Delele Worku AYELE, ed., Solomon Workneh FANTA, ed. and others. Advances of Science and Techology : 7th EAI International Conference, ICAST 2019, Proceedings. Cham: Springer, 2020, pp. 512-521. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST). 308. ISSN 1867-8211. eISSN 1867-822X. ISBN 978-3-030-43689-6. Available under: doi: 10.1007/978-3-030-43690-2_37
BibTex
@inproceedings{Birhanie2020Autom-59690,
  year={2020},
  doi={10.1007/978-3-030-43690-2_37},
  title={Automatic Amharic Part of Speech Tagging (AAPOST) : A Comparative Approach Using Bidirectional LSTM and Conditional Random Fields (CRF) Methods},
  number={308},
  isbn={978-3-030-43689-6},
  issn={1867-8211},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST)},
  booktitle={Advances of Science and Techology : 7th EAI International Conference, ICAST 2019, Proceedings},
  pages={512--521},
  editor={Gabbiye Habtu, Nigus and Ayele, Delele Worku and Fanta, Solomon Workneh},
  author={Birhanie, Worku Kelemework and Butt, Miriam}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/59690">
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59690/1/Birhanie_2-13uilhq9epned7.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-12T11:52:49Z</dcterms:available>
    <dc:rights>terms-of-use</dc:rights>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Butt, Miriam</dc:contributor>
    <dc:creator>Birhanie, Worku Kelemework</dc:creator>
    <dcterms:issued>2020</dcterms:issued>
    <dcterms:title>Automatic Amharic Part of Speech Tagging (AAPOST) : A Comparative Approach Using Bidirectional LSTM and Conditional Random Fields (CRF) Methods</dcterms:title>
    <dc:language>eng</dc:language>
    <dc:contributor>Birhanie, Worku Kelemework</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/45"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/59690"/>
    <dc:creator>Butt, Miriam</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59690/1/Birhanie_2-13uilhq9epned7.pdf"/>
    <dcterms:abstract xml:lang="eng">Part of speech (POS) tagging is an initial task for many natural language applications. POS tagging for Amharic is in its infancy. This study contributes towards the improvement of Amharic POS tagging by experimenting using Deep Learning and Conditional Random Fields (CRF) approaches. Word embedding is integrated into the system to enhance performance. The model was applied to an Amharic news corpus tagged into 11 major part of speeches and achieved accuracies of 91.12% and 90% for the Bidirectional LSTM and CRF methods respectively. The result shows that the Bidirectional LSTM approach performance is better than the CRF method. More enhancement is expected in the future by increasing the size and diversity of Amharic corpus.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/45"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-12T11:52:49Z</dc:date>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen