Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Admin for a login account.
This site is powered by Aigaion - A PHP/Web based management system for shared and annotated bibliographies. For more information visit www.aigaion.de. Get Web based bibliography management system at SourceForge.net. Fast, secure and Free Open Source software downloads
All publications sorted by recency

Nghiem, M-. Q. and Ananiadou, S., APLenty: annotation tool for creating high-quality datasets using active and proactive learning, in: Proceedings of Empirical Methods in Natural Language Processing (System Demonstrations), 2018
[URL]
Zerva, C. and Ananiadou, S., Paths for uncertainty: Exploring the intricacies of uncertainty identification for news, in: Proceedings of the NAACL Workshop on Computational Semantics Beyond Events and Roles (SemBEaR), pages 6-20, 2018
[URL]
Arase, Y. and Tsujii, J., Monolingual Phrase Alignment on Parse Forests, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1-11, 2017
[URL]
Przybyɫa, P., Soto, A. and Ananiadou, S., Identifying Personalised Treatments and Clinical Trials for Precision Medicine using Semantic Search with Thalia, in: Proceedings of the Twenty-Fifth Text REtrieval Conference (TREC 2017), National Insitute of Standards and Technology, 2017
[URL]
Ju., M., Miwa, M. and Ananiadou, S., A Neural Layered Model for Nested Named Entity Recognition, in: Proceedings of NAACL 2018, pages 1446-1459, 2018
[URL]
Li, M., Nguyen, N. T. H. and Ananiadou, S., Proactive Learning for Named Entity Recognition, in: Proceedings of BioNLP 2017, pages 117--125, Association for Computational Linguistics, 2017
[URL]
Batista-Navarro, R., Zerva, C., Nguyen, N. T. H. and Ananiadou, S., A Text Mining-Based Framework for Constructing an RDF-Compliant Biodiversity Knowledge Repository, pages 30-42, Springer, Communications in Computer and Information Science, volume 656, 2017
[DOI]
[URL]
Brockmeier, A. J., Mu, T., Ananiadou, S. and Goulermas, J. Y, Quantifying the Informativeness of Similarity Measurements (2017), in: Journal of Machine Learning Research, 18(1-61)
[URL]