Abstract The ability to capture time information is essential to many natural language processing and information retrieval applications. Therefore, a lexical resource associating word senses to their temporal orientation might be crucial for the computational tasks aiming at the interpretation of language of time in texts. In this paper, we propose a semi-supervised minimum cuts strategy that makes use of WordNet glosses and semantic relations to supplement WordNet entries with temporal information. Intrinsic and extrinsic evaluations show that our approach outperforms prior semi-supervised non-graph classifiers.

Url http://doras.dcu.ie/23235/1/Identifying%20Temporality%20of%20Word%20Senses%20Based%20on%20Minimum%20Cuts.pdf

Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning