Sappadla, Prateek Veeranna ; Nam, Jinseok ; Loza Mencía, Eneldo ; Fürnkranz, Johannes (2016)
Using Semantic Similarity for Multi-Label Zero-Shot Classification of Text Documents.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Sappadla, Prateek Veeranna ; Nam, Jinseok ; Loza Mencía, Eneldo ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | Using Semantic Similarity for Multi-Label Zero-Shot Classification of Text Documents |
| Sprache: | Deutsch |
| Publikationsjahr: | 2016 |
| Buchtitel: | Proceedings of the Tenth International Conference on Language Resources and Evaluation |
| Kurzbeschreibung (Abstract): | In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures. |
| ID-Nummer: | TUD-CS-2016-14783 |
| Fachbereich(e)/-gebiet(e): | DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 30 Nov 2017 17:50 |
| Letzte Änderung: | 13 Dez 2018 17:13 |
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