Ananiadou, S. - NaCTeM Publications - Aigaion 2.0

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 |

Nghiem, M-. Q., Baylis, P. und Ananiadou, S., Paladin: an annotation tool based on active and proactive learning, in: Proceedings of the 16th conference of the European Chapter of the Association for Computational Linguistics (EACL 2021): System Demonstrations, Seiten 238–243, 2021

[URL]

Alhuzali, H. und 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), Seiten 1573–1584, 2021

[URL]

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

[URL]

Hamulyak, E. N., Brockmeier, A. J., Killas, J. D., Ananiadou, S., Middeldorp, S. und Leroi, A. M., Women’s health in The BMJ: a data science history (2020), in: BMJ Open, 10(e039759)

[DOI]
[URL]

Li, M., Takamura, H. und Ananiadou, S., A Neural Model for Aggregating Coreference Annotation in Crowdsourcing, in: Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), Seiten 5760-5773, 2020

[URL]

Tran, T. T., Miwa, M. und Ananiadou, S., Syntactically-Informed Word Representations from Graph Neural Network (2020), in: Neurocomputing, 413(431-443)

[DOI]
[URL]

Trieu, H-L., Tran, T. T., Duong, K. N. A., Nguyen, A., Miwa, M. und Ananiadou, S., DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts (2020), in: Bioinformatics(btaa540)

[DOI]
[URL]

Zerva, C., Nghiem, M-. Q., Nguyen, N. T. H. und Ananiadou, S., Cited text span identification for scientific summarisation using pre-trained encoders (2020), in: Scientometrics

[DOI]
[URL]

Tran, T. T., Le, P. und Ananiadou, S., Revisiting Unsupervised Relation Extraction, in: Proceedings of ACL 2020, Seiten 7498–7505, 2020

[URL]

Thompson, P., Yates, T., Inan, E. und Ananiadou, S., Semantic Annotation for Improved Safety in Construction Work, in: Proceedings of LREC 2020, Seiten 1983‑1992, 2020

[URL]

Lambert, B. C., Kontonatsios, G., Mauch, M., Kokkoris, T., Jockers, M., Ananiadou, S. und Leroi, A. M., The pace of modern culture (2020), in: Nature Human Behaviour

[DOI]
[URL]

Leroi, A. M., Lambert, B. C., Mauch, M., Papadopoulou, M., Ananiadou, S., Lindberg, S. I. I. und Lindenfors, P., On revolutions (2020), in: Palgrave Communications, 6(4)

[DOI]
[URL]

Brockmeier, A. J., Ju., M., Przybyɫa, P. und Ananiadou, S., Improving reference prioritisation with PICO recognition (2019), in: BMC Medical Informatics and Decision Making, 19(256)

[DOI]
[URL]

Trieu, H-L., Duong, K. N. A., Nguyen, N. T. H., Miwa, M., Takamura, H. und Ananiadou, S., Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering, in: Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, Seiten 196-205, 2019

[URL]

Wang, Y., Fan, X., Chen, L., Chang, E. I-C., Ananiadou, S., Tsujii, J. und Xu, Y., Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries (2019), in: BMC Bioinformatics, 20(430)

[URL]

Espinosa, K., Miwa, M. und Ananiadou, S., A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection, in: Proceedings of EMNLP 2019, Seiten 3670-3677, 2019

[URL]

Christopoulou, F., Miwa, M. und Ananiadou, S., Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs, in: Proceedings of EMNLP 2019, Seiten 4927-4938, 2019

[URL]

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

[URL]

Christopoulou, F., Tran, T. T., Sahu, S. K., Miwa, M. und Ananiadou, S., Adverse Drug Events and Medication Relation Extraction in EHRs with Ensemble Deep Learning Methods (2019), in: Journal of the American Medical Informatics Association(ocz101)

[DOI]
[URL]

Ju., M., Nguyen, N. T. H., Miwa, M. und Ananiadou, S., An Ensemble of Neural Models for Nested Adverse Drug Events and Medication Extraction with Subwords (2019), in: Journal of the American Medical Informatics Association, 27:1(22-30)

[DOI]
[URL]

Sahu, S. K., Christopoulou, F., Miwa, M. und Ananiadou, S., Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network, in: Proceedings of ACL 2019, Seiten 4309-4316, 2019

[URL]

Ju., M., Short, A.D., Thompson, P., Bakerly, N. D., Gkoutos, G., Tsaprouni, L. und Ananiadou, S., Annotating and Detecting Phenotypic Information for Chronic Obstructive Pulmonary Disease (2019), in: JAMIA Open, 2:2(261-271)

[DOI]
[URL]

Li, M., Myrman, A. F., Mu, T. und Ananiadou, S., Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Seiten 2873-2883, 2019

[URL]

Iliopoulos, I., Ananiadou, S., Danchin, A., Ioannidis, J. P. A., Katsikis, P. D., Ouzounis, C. A. und Promponas, V. J., Point of View: Hypothesis, analysis and synthesis, it's all Greek to me (2019), in: eLife, 8(e43514)

[DOI]
[URL]

Bannach-Brown, A., Przybyɫa, P., Thomas, J., Rice, A., Ananiadou, S., Liao, J. und Macleod, M. R., Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error (2019), in: Systematic Reviews, 8(23)

[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: 0.0843 Sekunden.