Digitala Vetenskapliga Arkivet

Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Adapting Language Specific Components of Cross-Media Analysis Frameworks to Less-Resourced Languages: the Case of Amharic
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
2020 (English)In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020) / [ed] Dorothee Beermann, Laurent Besacier, Sakriani Sakti, and Claudia Soria, France, 2020, p. 298-305Conference paper, Published paper (Refereed)
Abstract [en]

We present an ASR based pipeline for Amharic that orchestrates NLP components within a cross media analysis framework (CMAF). One of the major challenges that are inherently associated with CMAFs is effectively addressing multi-lingual issues. As a result, many languages remain under-resourced and fail to leverage out of available media analysis solutions. Although spoken natively by over 22 million people and there is an ever-increasing amount of Amharic multimedia content on the Web, querying them with simple text search is difficult. Searching for, especially audio/video content with simple key words, is even hard as they exist in their raw form. In this study, we introduce a spoken and textual content processing workflow into a CMAF for Amharic. We design an ASR-named entity recognition (NER) pipeline that includes three main components: ASR, a transliterator and NER. We explore various acoustic modeling techniques and develop an OpenNLP-based NER extractor along with a transliterator that interfaces between ASR and NER. The designed ASR-NER pipeline for Amharic promotes the multi-lingual support of CMAFs. Also, the state-of-the art design principles and techniques employed in this study shed light for other less-resourced languages, particularly the Semitic ones.

Place, publisher, year, edition, pages
France, 2020. p. 298-305
Keywords [en]
Speech recognition, named entity recognition, Less-resourced languages, Amharic, Cross-media analysis
National Category
Computer Sciences
Research subject
computational linguistics
Identifiers
URN: urn:nbn:se:umu:diva-170765ISBN: 979-10-95546-35-1 (print)OAI: oai:DiVA.org:umu-170765DiVA, id: diva2:1430423
Conference
Language Resources and Evaluation Conference (LREC 2020), Marseille, France 11–16 May, 2020
Available from: 2020-05-15 Created: 2020-05-15 Last updated: 2020-05-15Bibliographically approved

Open Access in DiVA

fulltext(60110 kB)13 downloads
File information
File name FULLTEXT01.pdfFile size 60110 kBChecksum SHA-512
12f390e10bd520d1767a594ee4d2a05394bf983892dc2cc655fce27572201f5fa779a1c34ab8abde350eecc0be86603e9220851d03e11c65be39e363992a6666
Type fulltextMimetype application/pdf

Other links

https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/SLTUCCURLbook.pdf

Search in DiVA

By author/editor
Woldemariam, Yonas DemekeDahlgren, Adam
By organisation
Department of Computing Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 13 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 16 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf