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 | 376-400 | 401-407 |

Shardlow, M., Przybyɫa, P., Batista-Navarro, R., Carter, J., McNaught, J. and Ananiadou, S., Facilitating and promoting web annotation with Argo, in: Proceedings of I Annotate 2016, 2016

Batista-Navarro, R., Kontonatsios, G., MihÄƒilÄƒ, C., Thompson, P., Rak, R., Nawaz, R., Korkontzelos, I. and Ananiadou, S., Facilitating the Analysis of Discourse Phenomena in an Interoperable NLP Platform, in: Computational Linguistics and Intelligent Text Processing, páginas 559-571, Springer Berlin Heidelberg, 2013

[DOI]
[URL]

Tsuruoka, Y., Tsujii, J. and Ananiadou, S., FACTA: a text search engine for finding associated biomedical concepts (2008), in: Bioinformatics, 24:21(2259-60)

Luo, Z., Xie, Q. and Ananiadou, S., Factual consistency evaluation of summarization in the Era of large language models (2024), in: Expert Systems with Applications, 254(124456)

[DOI]
[URL]

Tsuruoka, Y., Tsujii, J. and Ananiadou, S., Fast Full Parsing by Linear-Chain Conditional Random Fields, in: Proceedings of the 12th Conference of European Chapter of the Association for Computational Linguistics (EACL-09), páginas 790--798, 2009

[URL]

Kano, Y., Nguyen, N. T. H., SÃ¦tre, R., Yoshida, K., Miyao, Y., Tsuruoka, Y., Matsubayashi, Y., Ananiadou, S. and Tsujii, J., Filling the gaps between tools and users: a tool comparator, using protein-protein interaction as an example., in: Proceedings of the Pacific Symposium on Biocomputing 2008 (PSB 2008), páginas 616--627, 2008

Xie, Q., Han, W., Chen, Z., Xiang, R., Zhang, X., He, Y., Xiao, M., Li, D., Dai, Y., Feng, D., Xu, Y., Kang, H., Kuang, Z., Yuan, C., Yang, K, Luo, Z., Zhang, T., Liu, Z., Xiong, G., Deng, Z., Jiang, Y., Yao, Z., Li, H., Yu, Y., Lopez-Lira, A., Wang, B., Lai, Y., Wang, H., Peng, M., Ananiadou, S. and Huang, J., FinBen: An Holistic Financial Benchmark for Large Language Models, in: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

[URL]

Xie, Q., Huang, J., Li, D., Chen, Z., Xiang, R., Xiao, M., Yu, Y., Somasundaram, V., Yang, K, Yuan, C., Luo, Z., Liu, Z., He, Y., Jiang, Y., Li, H., Feng, D., Liu, X. -Y., Wang, B., Wang, H., Lai, Y., Suchow, J., Lopez-Lira, A., Peng, M. and Ananiadou, S., FinNLP-AgentScen-2024 Shared Task: Financial Challenges in Large Language Models - FinLLMs, in: Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning, páginas 119- 126, 2024

[URL]

Liu, Z., Zhang, X., Yang, K, Xie, Q., Huang, J. and Ananiadou, S., FMDLlama: Financial Misinformation Detection based on Large Language Models, arXiv, 2024

[URL]

Cao, J., Peek, N., Renehan, A. G and Ananiadou, S., Gaussian Distributed Prototypical Network for Few-shot Genomic Variant Detection, in: Proceedings of BioNLP 2023, páginas 26–36, 2023

[URL]

Bishop, J., Xie, Q. and Ananiadou, S., GenCompareSum: a hybrid unsupervised summarization method using salience, in: Proceedings of BioNLP 2022, páginas 220–240, 2022

[URL]

Stenetorp, P., Pyysalo, S., Ananiadou, S. and Tsujii, J., Generalising semantic category disambiguation with large lexical resources for fun and profit (2014), in: Journal of Biomedical Semantics, 5(26)

[DOI]
[URL]

Meziane, F., Athanasakis, N. and Ananiadou, S., Generating Natural Language specifications from UML class diagrams (2008), in: Requirements Engineering, 13:1(1--18)

Cao, J. and Ananiadou, S., GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction, in: Findings of EMNLP, páginas 2119–2126, 2021

[DOI]
[URL]

Liu, B, Schlegel, V., Thompson, P., Batista-Navarro, R. and Ananiadou, S., Global information-aware argument mining based on a top-down multi-turn QA model (2023), in: Information Processing & Management, 60:5(103445)

[DOI]
[URL]

Luo, Z., Liu, L., Ananiadou, S. and Xie, Q., Graph Contrastive Topic Model (2024), in: Expert Systems with Applications, 255:Part C(124631)

[DOI]
[URL]

Xie, Q., Huang, J., Saha, T and Ananiadou, S., GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization, in: Proceedings of COLING, páginas 6259–6269, 2022

[URL]

Wang, Y., Feng, D., Dai, Y., Chen, Z., Huang, J., Ananiadou, S., Xie, Q. and Wang, H., HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection, in: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

[URL]

Black, W.J., Rupp, C. J., Nobata, C., McNaught, J., Tsujii, J. and Ananiadou, S., High-Precision Semantic Search by Generating and Testing Questions, in: Proceedings of the UK e-Science All Hands Meeting, 2010

[URL]

Tekiner, F., Ananiadou, S., Tsuruoka, Y. and Tsujii, J., Highly Scalable Text Mining Parallel Tagging Application, in: Proceedings of ICSSCW2009, Famgusta, Cyprus, 2009

[URL]

Yu, Z. and Ananiadou, S., How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning, in: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, páginas 3281–3292, 2024

[DOI]
[URL]

Sasaki, Y., Tsuruoka, Y., McNaught, J. and Ananiadou, S., How to make the most of NE dictionaries in statistical NER (2008), in: BMC Bioinformatics, 9:Suppl 11(S5)

Sasaki, Y., Tsuruoka, Y., McNaught, J. and Ananiadou, S., How to Make the Most of NE Dictionaries in Statistical NER, in: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (BioNLP 2008), páginas 63--70, Association for Computational Linguistics, 2008

Inan, E., Thompson, P., Yates, T. and Ananiadou, S., HSEarch: semantic search system for workplace accident reports, in: Proceedings of the 43rd European Confererence on Information Retrieval (ECIR 2021), páginas 514-519, 2021

[URL]

Thompson, P. and Ananiadou, S., HYPHEN: A flexible, hybrid method to map phenotype concept mentions to terminological resources (2018), in: Terminology, 24:1(91-121)

[DOI]
[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 | 401-407 |

tempo de processamento: 0.0784 segundos.