579 research outputs found
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks
Given the development and abundance of social media, studying the stance of
social media users is a challenging and pressing issue. Social media users
express their stance by posting tweets and retweeting. Therefore, the
homogeneous relationship between users and the heterogeneous relationship
between users and tweets are relevant for the stance detection task. Recently,
graph neural networks (GNNs) have developed rapidly and have been applied to
social media research. In this paper, we crawl a large-scale dataset of the
2020 US presidential election and automatically label all users by manually
tagged hashtags. Subsequently, we propose a bipartite graph neural network
model, DoubleH, which aims to better utilize homogeneous and heterogeneous
information in user stance detection tasks. Specifically, we first construct a
bipartite graph based on posting and retweeting relations for two kinds of
nodes, including users and tweets. We then iteratively update the node's
representation by extracting and separately processing heterogeneous and
homogeneous information in the node's neighbors. Finally, the representations
of user nodes are used for user stance classification. Experimental results
show that DoubleH outperforms the state-of-the-art methods on popular
benchmarks. Further analysis illustrates the model's utilization of information
and demonstrates stability and efficiency at different numbers of layers
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
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