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Annika M Schoene
Annika M Schoene
Northeastern University
Verified email at northeastern.edu
Title
Cited by
Cited by
Year
Natural language processing applied to mental illness detection: a narrative review
T Zhang, AM Schoene, S Ji, S Ananiadou
NPJ digital medicine 5 (1), 1-13, 2022
1472022
Automatic identification of suicide notes from linguistic and sentiment features
AM Schoene, N Dethlefs
Proceedings of the 10th SIGHUM workshop on language technology for cultural …, 2016
512016
Automatic identification of suicide notes with a transformer-based deep learning model
T Zhang, AM Schoene, S Ananiadou
Internet interventions 25, 100422, 2021
222021
Hierarchical multiscale recurrent neural networks for detecting suicide notes
AM Schoene, A Turner, GR De Mel, N Dethlefs
IEEE Transactions on Affective Computing, 2021
212021
Bidirectional Dilated LSTM with Attention for Fine-grained Emotion Classification in Tweets.
AM Schoene, AP Turner, N Dethlefs
Affcon@ aaai 2614, 100-117, 2020
152020
A divide-and-conquer approach to neural natural language generation from structured data
N Dethlefs, A Schoene, H Cuayįhuitl
Neurocomputing 433, 300-309, 2021
132021
Dilated lstm with attention for classification of suicide notes
AM Schoene, G Lacey, AP Turner, N Dethlefs
Proceedings of the tenth international workshop on health text mining and …, 2019
132019
NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding
K Wang, R Stevens, H Alachram, Y Li, L Soldatova, R King, S Ananiadou, ...
NPJ systems biology and applications 7 (1), 38, 2021
102021
A narrative literature review of natural language processing applied to the occupational exposome
AM Schoene, I Basinas, M Van Tongeren, S Ananiadou
International journal of environmental research and public health 19 (14), 8544, 2022
92022
Unsupervised suicide note classification
AM Schoene, N Dethlefs
WISDOM'18 (KDD 2018, August 20th, London UK), 2018
72018
Classifying suicide-related content and emotions on Twitter using Graph Convolutional Neural Networks
AM Schoene, L Bojanić, MQ Nghiem, IM Hunt, S Ananiadou
IEEE Transactions on Affective Computing, 2022
62022
Emerging trends: Unfair, biased, addictive, dangerous, deadly, and insanely profitable
K Church, A Schoene, JE Ortega, R Chandrasekar, V Kordoni
Natural Language Engineering 29 (2), 483-508, 2023
52023
Natural language processing applied to mental illness detection: A narrative review. npj Digital Medicine, 5
T Zhang, A Schoene, S Ji, S Ananiadou
52022
Hybrid approaches to fine-grained emotion detection in social media data
AM Schoene
Twenty-Fifth AAAI/SIGAI Doctoral Consortium, 2020
42020
An example of (too much) hyper-parameter tuning in suicide ideation detection
AM Schoene, J Ortega, S Amir, K Church
Proceedings of the International AAAI Conference on Web and Social Media 17 …, 2023
22023
Evolutionary constraint in artificial gene regulatory networks
AP Turner, G Lacey, A Schoene, N Dethlefs
UK Workshop on Computational Intelligence, 29-40, 2018
22018
RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification
AM Schoene, N Dethlefs, S Ananiadou
Proceedings of the Thirteenth Language Resources and Evaluation Conference …, 2022
12022
Improving the Transparency of Deep Neural Networks using Artificial Epigenetic Molecules.
G Lacey, AM Schoene, N Dethlefs, AP Turner
IJCCI, 167-175, 2020
12020
Dilated LSTM with ranked units for Classification of Suicide Notes
AM Schoene, AP Turner, N Dethlefs
AI for Social Good at NeurIPS, 2019
12019
Pooling tweets by fine-grained emotions to uncover topic trends in social media
A Schoene, G de Mel
2019 22th International Conference on Information Fusion (FUSION), 1-7, 2019
12019
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