Abstract
Scientific documents often adopt a well-defined vocabulary and avoid the use of ambiguous terms. However, as soon as documents from different research sub-communities are considered in combination, many scientific terms become ambiguous as the same term can refer to different concepts from different sub-communities. The ability to correctly identify the right sense of a given term can considerably improve the effectiveness of retrieval models, and can also support additional features such as search diversification. This is even more critical when applied to explorative search systems within the scientific domain.
In this paper, we propose novel semi-supervised methods to term disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. Experimental evidence over two different test collections from the physics and biomedical domains shows that the proposed method is effective and outperforms state-of-the-art approaches based on feature vectors constructed out of term co-occurrences as well as standard supervised approaches.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Abdalgader, K., Skabar, A.: Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance. ACM Trans. Speech Lang. Process. 9(1), 2:1–2:21 (2012)
Bruce, R.F., Wiebe, J.M.: Decomposable modeling in natural language processing. Comput. Linguist. 25(2), 195–207 (1999)
Daelemans, W., Van Den Bosch, A., Zavrel, J.: Forgetting exceptions is harmful in language learning. Mach. Learn. 34(1-3), 11–41 (1999)
Fellbaum, C.: Wordnet. Theory and Applications of Ontology: Computer Applications, 231–243 (2010)
Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: SIGIR, pp. 765–774. ACM, New York (2011)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Bibsonomy: A social bookmark and publication sharing system. In: Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, pp. 87–102 (2006)
Yepes, A.J., Aronson, A.R.: Knowledge-based and knowledge-lean methods combined in unsupervised word sense disambiguation. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, IHI 2012, pp. 733–736. ACM, New York (2012)
Jimeno-Yepes, A.J., McInnes, B.T., Aronson, A.R.: Exploiting mesh indexing in medline to generate a data set for word sense disambiguation. BMC Bioinformatics 12, 223 (2011)
Lee, Y.K., Ng, H.T.: An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In: ACL 2002, EMNLP 2002, Stroudsburg, PA, USA, vol. 10, pp. 41–48. Association for Computational Linguistics (2002)
Navigli, R.: Word sense disambiguation: A survey. ACM Comput. Surv. 41(2), 10:1–10:69 (2009)
Navigli, R., Faralli, S., Soroa, A., de Lacalle, O., Agirre, E.: Two birds with one stone: learning semantic models for text categorization and word sense disambiguation. In: CIKM, pp. 2317–2320. ACM, New York (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prokofyev, R., Demartini, G., Boyarsky, A., Ruchayskiy, O., Cudré-Mauroux, P. (2013). Ontology-Based Word Sense Disambiguation for Scientific Literature. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-36973-5_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
eBook Packages: Computer ScienceComputer Science (R0)