3 research outputs found
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text
Adverse reaction caused by drugs is a potentially dangerous problem which may
lead to mortality and morbidity in patients. Adverse Drug Event (ADE)
extraction is a significant problem in biomedical research. We model ADE
extraction as a Question-Answering problem and take inspiration from Machine
Reading Comprehension (MRC) literature, to design our model. Our objective in
designing such a model, is to exploit the local linguistic context in clinical
text and enable intra-sequence interaction, in order to jointly learn to
classify drug and disease entities, and to extract adverse reactions caused by
a given drug. Our model makes use of a self-attention mechanism to facilitate
intra-sequence interaction in a text sequence. This enables us to visualize and
understand how the network makes use of the local and wider context for
classification.Comment: 7 pages, 5 figures, 4 table
Compositional Attention Networks for Interpretability in Natural Language Question Answering
MAC Net is a compositional attention network designed for Visual Question
Answering. We propose a modified MAC net architecture for Natural Language
Question Answering. Question Answering typically requires Language
Understanding and multi-step Reasoning. MAC net's unique architecture - the
separation between memory and control, facilitates data-driven iterative
reasoning. This makes it an ideal candidate for solving tasks that involve
logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of
MAC net as a data-efficient and interpretable architecture for Natural Language
Question Answering. The transparent nature of MAC net provides a highly
granular view of the reasoning steps taken by the network in answering a query.Comment: 8 pages,10 figures, 1 tabl
Detecting Parking Spaces in a Parcel using Satellite Images
Remote Sensing Images from satellites have been used in various domains for
detecting and understanding structures on the ground surface. In this work,
satellite images were used for localizing parking spaces and vehicles in
parking lots for a given parcel using an RCNN based Neural Network
Architectures. Parcel shapefiles and raster images from USGS image archive were
used for developing images for both training and testing. Feature Pyramid based
Mask RCNN yields average class accuracy of 97.56% for both parking spaces and
vehicle