Ananiadou, S. - NaCTeM Publications

ANSICHT
EXPORT
- Alle Publikationen exportieren
- Autor exportieren
SORTIERE NACH
- Autor
- Titel
- Typ/Zeitschrift
- Jahr
- Erstelldatum
SYSTEM
- Hilfe
- Über diese Seite
GASTNUTZER
Guest
ANMELDUNG
Name:

Passwort:

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

Vorname(n):	S.
Nachname(n):	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 |

Liu, B, Schlegel, V., Batista-Navarro, R. und Ananiadou, S., Entity Coreference and Co-occurrence Aware Argument Mining from Biomedical Literature, in: Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023, 2023

[URL]

Goldsack, T., Luo, Z., Xie, Q., Scarton, C., Shardlow, M., Ananiadou, S. und Lin, C., BioLaySumm 2023 Shared Task: Lay Summarisation of Biomedical Research Articles, in: Proceedings of BioNLP 2023, Seiten 468–477, 2023

[URL]

Miwa, M. und Ananiadou, S., DISTANT: Distantly Supervised Entity Span Detection and Classification, in: Proceedings of BioNLP 2023, Seiten 171–177, 2023

[URL]

Yuan, C., Xie, Q. und Ananiadou, S., Zero-shot Temporal Relation Extraction with ChatGPT, in: Proceedings of BioNLP 2023, Seiten 92–102, 2023

[URL]

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

[URL]

Zhang, T., Yang, K und Ananiadou, S., Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media, in: Proceedings of BioNLP 2023, Seiten 114–126, 2023

[URL]

Zhang, T., Yang, K, Alhuzali, H., Liu, B und Ananiadou, S., PHQ-aware depressive symptoms identification with similarity contrastive learning on social media (2023), in: Information Processing & Management(103417)

[DOI]
[URL]

Vasilakes, J., Georgiadis, P., Nguyen, N. T. H., Miwa, M. und Ananiadou, S., Contextualized Medication Event Extraction with Levitated Markers (2023), in: Journal of Biomedical Informatics(104347)

[DOI]
[URL]

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

[URL]

Alhuzali, H., Zhang, T. und 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]

Luo, Z., Xie, Q. und Ananiadou, S., Readability Controllable Biomedical Document Summarization, in: Findings of the Association for Computational Linguistics: EMNLP 2022, Seiten 4667–4680, 2022

[URL]

Cao, J., van Veen, E. M., Peek, N., Renehan, A. G und Ananiadou, S., A Novel Automated Approach to Mutation-Cancer Relation Extraction by Incorporating Heterogeneous Knowledge (2023), in: IEEE Journal of Biomedical and Health Informatics, 27:2(1096 - 1105)

[DOI]

Schoene, A, Bojanic, L., Nghiem, M-. Q., Hunt, I. M. und Ananiadou, S., Classifying suicide-related content and emotions on Twitter using Graph Convolutional Neural Networks (2022), in: IEEE Transactions on Affective Computing(1-12)

[DOI]
[URL]

Vasquez-Rodriguez, L., Nguyen, N. T. H., Shardlow, M. und Ananiadou, S., UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification, in: Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), Seiten 218-224, 2022

[URL]

Luo, Z., Xie, Q. und Ananiadou, S., CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization, in: Proceedings of the ACM Web Conference, Seiten 1843–1852, 2023

[DOI]
[URL]

Nguyen, N. T. H., Miwa, M. und Ananiadou, S., Span-based Named Entity Recognition by Generating and Compressing Information, in: Proceedings of EACL 2023, Seiten 1984–1996, 2023

[URL]

Zhang, T., Yang, K, Shaoxiong, J. und Ananiadou, S., Emotion fusion for mental illness detection from social media: A survey (2023), in: Information Fusion, 92(231-246)

[DOI]
[URL]

Yang, K, Zhang, T., Alhuzali, H. und Ananiadou, S., Cluster-Level Contrastive Learning for Emotion Recognition in Conversations (2023), in: IEEE Transactions on Affective Computing(1-12)

[DOI]
[URL]

Alhuzali, H., Zhang, T. und 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]

Cao, J., van Veen, E. M., Peek, N., Renehan, A. G und Ananiadou, S., EPICURE: Ensemble Pretrained Models for Extracting Cancer Mutations from Literature, in: Proceedings of IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Seiten 461-467, 2021

[DOI]
[URL]

Zhang, T., Schoene, A und Ananiadou, S., Automatic identification of suicide notes with a transformer-based deep learning model (2021), in: Internet Interventions(100422)

[DOI]
[URL]

Zerva, C., Taylor, S., Soto, A., Nguyen, N. T. H. und Ananiadou, S., A term-based and citation network-based search system for COVID-19 (2021), in: JAMIA Open, 4:4(ooab104)

[DOI]
[URL]

Wang, K., Stevens, R., Alachram, H., Li, Y., Soldatova, L. N., King, R. D., Ananiadou, S., Schoene, A, Li, M., Christopoulou, F., Ambite, J. L., Matthew, J., Garg, S., Hermjakob, U., Marcu, D., Sheng, E., Beissbarth, T., Wingender, E., Galstyan, A., Gao, X., Chambers, B., Pan, W., Khomtchouk, B. B., Evans, J. A. und Rzhetsky, A., NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding (2021), in: npj systems biology and applications, 7(38)

[DOI]
[URL]

Navarro, T., Parrish, R., Cotoi, C., Germini, F., Linkins, L., Iorio, A., Haynes, R. B., Ananiadou, S. und Lokker, C., A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation (2021), in: JMIR Research Protocols, 10:11(e29398)

[DOI]
[URL]

Trieu, H-L., Miwa, M. und Ananiadou, S., BioVAE: a pre-trained latent variable language model for biomedical text mining (2022), 38:3(872-874)

[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 | 376-389 |

Ausführdauer: 0.0803 Sekunden.