Ontology-Based Word Sense Disambiguation for Scientific Literature
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.
Unable to display preview. Download preview PDF.
- 1.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)Google Scholar
- 2.Bruce, R.F., Wiebe, J.M.: Decomposable modeling in natural language processing. Comput. Linguist. 25(2), 195–207 (1999)Google Scholar
- 4.Fellbaum, C.: Wordnet. Theory and Applications of Ontology: Computer Applications, 231–243 (2010)Google Scholar
- 5.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)Google Scholar
- 6.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)Google Scholar
- 9.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)Google Scholar
- 10.Navigli, R.: Word sense disambiguation: A survey. ACM Comput. Surv. 41(2), 10:1–10:69 (2009)Google Scholar
- 11.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)Google Scholar