Ananiadou, S. - NaCTeM Publications

NAVEGAR
EXPORTAR
- Exportar todas as publicações
- Export author
ORDENAR POR
- Autor
- Título
- Tipo/journal
- Ano
- Mais recente
SITE
- Ajuda
- Sobre esse site
USUÁRIO GUEST
Guest
ENTRE
Nome:

Senha:

Deutsch, English, Nederlands, Norsk, Português, <mais...>

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.

Primeiro nome(s):	S.
Sobrenome(s):	Ananiadou

| 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 | 376-389 |

Alhuzali, H. and Ananiadou, S., SpanEmo: Casting Multi-label Emotion Classification as Span-prediction, in: Proceedings 16th conference of the European Chapter of the Association for Computational Linguistics (EACL 2021), páginas 1573–1584, 2021

[URL]

Alhuzali, H. and Ananiadou, S., Improving Textual Emotion Recognition Based on Intra- and Inter-Class Variation (2021), in: IEEE Transactions on Affective Computing

[DOI]
[URL]

Alhuzali, H. and Ananiadou, S., Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning, in: Proceedings of BioNLP, páginas 339-347, 2019

[URL]

Alhuzali, H., Zhang, T. and Ananiadou, S., A comparative geolocation and text mining analysis of emotions and topics during the COVID-19 Pandemic in the UK (2022), in: Journal of Medical Internet Research

[DOI]

Alhuzali, H., Zhang, T. and Ananiadou, S., Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis (2022), in: Journal of Medical Internet Research, 24:10(e40323)

[DOI]
[URL]

Alhuzali, H., Zhang, T. and Ananiadou, S., Predicting Sign of Depression via Using Frozen Pre-trained Models and Random Forest Classifier., in: CLEF (Working Notes), 2021

[URL]

Almeida, B., Ananiadou, S., Bagnato, A., Barbero, A. B., Di Rocco, J., Di Ruscio, D., Kolovos, D. S., Korkontzelos, I., Hansen, S., Malo, P., Matragkas, N., Paige, R. F. and Vinju, J., OSSMETER: Automated Measurement and Analysis of Open Source Software, in: Proceedings of the Projects Showcase at the Software Technologies: Applications and Foundations 2015 (STAF 2015), 2015

[URL]

Alnazzawi, N., Thompson, P. and Ananiadou, S., Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource (2016), in: PLOS ONE, 11:9(e0162287)

[DOI]
[URL]

Alnazzawi, N., Thompson, P. and Ananiadou, S., Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature, in: Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi), Gothenburg, Sweden, páginas 69-74, Association for Computational Linguistics, 2014

[URL]

Alnazzawi, N., Thompson, P., Batista-Navarro, R. and Ananiadou, S., Using text mining techniques to extract phenotypic information from the PhenoCHF corpus (2015), in: BMC Medical Informatics and Decision Making, 15:Suppl. 2(S3)

[DOI]
[URL]

Ananiadou, S., The National Centre for Text Mining: a Vision for the Future (2007), in: Ariadne, 53

[URL]

Ananiadou, S., Chruszcz, J., Keane, J., McNaught, J. and Watry, P., The National Centre for Text Mining: Aims and Objectives (2005), in: Ariadne, 42

[URL]

Ananiadou, S., Cotter, P., Nobata, C., Okazaki, N., Rea, B., Sasaki, Y., Tsuruoka, Y. and Tsujii, J., SemText: a semantically enriched information retrieval system for biology, in: Proceedings of the 8th International Conference on Systems Biology (ICSB 2007), 2007

Second International Symposium on Semantic Mining in Biomedicine (SMBM), 2006

[URL]

EntitÃ©s NommÃ©es, ATALA, Traitement Automatique des Langues, volume 54, 2013

[URL]

Ananiadou, S., Friedman, C. and Tsujii, J., Introduction: named entity recognition in biomedicine (2004), in: Journal of Biomedical Informatics, 37:6(393--395)

[URL]

Ananiadou, S., Kell, D. B. and Tsujii, J., Text mining and its potential applications in systems biology (2006), in: Trends in Biotechnology, 24:12(571 - 579)

Text Mining for Biology And Biomedicine, Artech House, Inc., 2006

[URL]

Ananiadou, S. and McNaught, J., Introduction to Text Mining in Biology, in: Text Mining for Biology and Biomedicine, páginas 1--12, Artech House, Inc., 2006

Ananiadou, S., McNaught, J., Thomas, J. and Oliver, S., Evaluating a text mining based educational search portal, in: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), páginas 3344--3350, 2010

[URL]

Ananiadou, S., McNaught, J. and Thompson, P., The English Language in the Digital Age, in: White Paper Series, Springer, 2012

[URL]

Ananiadou, S. and NenadiÄ‡, G., Automatic Terminology Management in Biomedicine, in: Text Mining for Biology and Biomedicine, páginas 67--98, Artech House, Inc., 2006

Ananiadou, S., Ohta, T. and Rutter, M., Text Mining supporting Search for Knowledge Discovery in Diabetes (2012), in: Current Cardiovascular Risk Reports

[URL]

Ananiadou, S., Okazaki, N., Procter, R., Rea, B. and Thomas, J., Supporting Systematic Reviews using Text Mining (2009), in: Social Science Computer Review, 27:4(509-523)

[DOI]
[URL]

Ananiadou, S., Text Mining for Biomedicine (2009), in: Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration(1--11)

[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 | 376-389 |

tempo de processamento: 0.0699 segundos.