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dc.contributor.authorHenriksson, Aron
dc.contributor.authorMoen, Hans
dc.contributor.authorSkeppstedt, Maria
dc.contributor.authorDaudaravicius, Vidas
dc.contributor.authorDuneld, Martin
dc.identifier.citationJournal of Biomedical Semantics. 2014, 5 (6).nb_NO
dc.description.abstractBackground: Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results: A combination of two distributional models – Random Indexing and Random Permutation – employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora – a corpus of clinical text and a corpus of medical journal articles – further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions: This study demonstrates that ensembles of semantic spaces can yield improved performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. This notion, which merits further exploration, allows different distributional models – with different model parameters – and different types of corpora to be combined, potentially allowing enhanced performance to be obtained on a wide range of natural language processing tasks.nb_NO
dc.publisherBioMed Centralnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleSynonym Extraction and Abbreviation Expansion with Ensembles of Semantic Spacesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalJournal of Biomedical Semanticsnb_NO
dc.relation.projectNorges forskningsråd: 193022nb_NO
dc.description.localcode© 2014 Henriksson et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.nb_NO
cristin.unitnameInstitutt for datateknologi og informatikk

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Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal