63 citations · 158 across the 14 of their papers we have counts for
17 papers
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
Natalia Loukachevitch, Suresh Manandhar, Elina Baral +5
This paper describes NEREL-BIO -- an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain da…
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura +6
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it co…
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
Lars Malmqvist, Tommy Yuan, Suresh Manandhar
This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated us…
FatNet: A Feature-attentive Network for 3D Point Cloud Processing
Chaitanya Kaul, Nick Pears, Suresh Manandhar
The application of deep learning to 3D point clouds is challenging due to its lack of order. Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we prop…
FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation
Chaitanya Kaul, Nick Pears, Hang Dai +2
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation. We combine attention mechanisms wi…
Penalizing small errors using an Adaptive Logarithmic Loss
Chaitanya Kaul, Nick Pears, Hang Dai +2
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth. Fundamentally, they define a functional landscape for travers…