Ananiadou, S.
Vorname(n): S.
Nachname(n): Ananiadou

Publikationen von Ananiadou, S. sortiert nach Titel
| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-425 | 426-442 |


I

Ananiadou, S., Thompson, P. und Nawaz, R., Improving Search Through Event-based Biomedical Text Mining, in: Proceedings of the 1st Automated Motif Discovery in Cultural Heritage and Scientific Communication Texts (AMICUS) workshop, 2010
attachment
Kano, Y., McCrochon, L., Ananiadou, S. und Tsujii, J., Integrated NLP Evaluation System for Pluggable Evaluation Metrics with Extensive Interoperable Toolkit, in: Proceedings of the NAACL HLT Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing, Seiten 22--30, 2009
[URL]
Piao, S., Ananiadou, S. und McNaught, J., Integrating Annotation Tools into UIMA for Interoperability, in: Proceedings of the UK e-Science AHM Conference 2007, Seiten 575--582, 2007
attachment
Rak, R., Rowley, A., Carter, J., Batista-Navarro, R. und Ananiadou, S., Interoperability and Customisation of Annotation Schemata in Argo, in: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland, Seiten 3837-3842, European Language Resources Association (ELRA), 2014
[URL]
Yu, Z. und Ananiadou, S., Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis, in: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Seiten 3293–3306, 2024
[DOI]
[URL]
Ananiadou, S. und McNaught, J., Introduction to Text Mining in Biology, in: Text Mining for Biology and Biomedicine, Seiten 1--12, Artech House, Inc., 2006
Ananiadou, S., Friedman, C. und Tsujii, J., Introduction: named entity recognition in biomedicine (2004), in: Journal of Biomedical Informatics, 37:6(393--395)
[URL]
Vasquez-Rodriguez, L., Shardlow, M., Przybyɫa, P. und Ananiadou, S., Investigating Text Simplification Evaluation, in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Seiten 876-882, 2021
[URL]
Yano, K., Miwa, M. und Ananiadou, S., IRIS: Rapid Curation Framework for Iterative Improvement of Noisy Named Entity Annotations, in: Proceedings of the International Conference on Applications of Natural Language to Information Systems, Seiten 58-69, 2025
[DOI]
[URL]

K

Nobata, C., Cotter, P., Okazaki, N., Rea, B., Sasaki, Y., Tsuruoka, Y., Tsujii, J. und Ananiadou, S., Kleio: a knowledge-enriched information retrieval system for biology, in: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Singapore, Singapore, Seiten 787--788, ACM, 2008
attachment
Inan, E., Thompson, P., Christopoulou, F., Yates, T. und Ananiadou, S., Knowledge Graph Enrichment of a Semantic Search System for Construction Safety, in: Intelligent Systems and Applications. IntelliSys 2022., Seiten 33-52, Springer, Cham, 2022
[DOI]
[URL]

L

Spasić, I., Nenadić, G. und Ananiadou, S., Learning to Classify Biomedical Terms through Literature Mining and Genetic Algorithms, in: Intelligent Data Engineering and Automated Learning – IDEAL 2004, Seiten 345--351, Springer-Verlag, 2004
attachment
Espinosa, K., Batista-Navarro, R. und Ananiadou, S., Learning to recognise named entities in Tweets by exploiting weakly labelled data, in: Proceedings of the 2nd Workshop on Noisy User-generated Text (W-NUT 2016), 2016
[URL]
| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-425 | 426-442 |