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Replicating relevance-ranked synonym discovery in a new language and domain
Max Planck Institut für Informatik, DEU.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag , 2019, Vol. 11437, p. 429-442Conference paper, Published paper (Refereed)
Abstract [en]

Domain-specific synonyms occur in many specialized search tasks, such as when searching medical documents, legal documents, and software engineering artifacts. We replicate prior work on ranking domain-specific synonyms in the consumer health domain by applying the approach to a new language and domain: identifying Swedish language synonyms in the building construction domain. We chose this setting because identifying synonyms in this domain is helpful for downstream systems, where different users may query for documents (e.g., engineering requirements) using different terminology. We consider two new features inspired by the change in language and methodological advances since the prior work’s publication. An evaluation using data from the building construction domain supports the finding from the prior work that synonym discovery is best approached as a learning to rank task in which a human editor views ranked synonym candidates in order to construct a domain-specific thesaurus. We additionally find that FastText embeddings alone provide a strong baseline, though they do not perform as well as the strongest learning to rank method. Finally, we analyze the performance of individual features and the differences in the domains. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer Verlag , 2019. Vol. 11437, p. 429-442
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keywords [en]
Domain-specific search, Generalization, Replication, Synonym discovery, Thesaurus construction, Construction, Information retrieval, Software engineering, Thesauri, Building construction, Domain specific searches, Individual features, Learning to rank, Medical documents, Semantics
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-17895DOI: 10.1007/978-3-030-15712-8_28Scopus ID: 2-s2.0-85064883724ISBN: 9783030157111 (print)OAI: oai:DiVA.org:bth-17895DiVA, id: diva2:1316774
Conference
41st European Conference on Information Retrieval, ECIR; Cologne; Germany; 14 April 2019 through 18 April
Available from: 2019-05-21 Created: 2019-05-21 Last updated: 2019-05-21Bibliographically approved

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Unterkalmsteiner, Michael

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • 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