Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Can Graph Neural Networks Help Document Retrieval: A Case Study on
  CORD19 with Concept Map Generation   Graph neural networks (GNNs), as a group of powerful tools for representation
learning on irregular data, have manifested superiority in various downstream
tasks. With unstructured texts represented as concept maps, GNNs can be
exploited for tasks like document retrieval. Intrigued by how can GNNs help
document retrieval, we conduct an empirical study on a large-scale
multi-discipline dataset CORD-19. Results show that instead of the complex
structure-oriented GNNs such as GINs and GATs, our proposed semantics-oriented
graph functions achieve better and more stable performance based on the BM25
retrieved candidates. Our insights in this case study can serve as a guideline
for future work to develop effective GNNs with appropriate semantics-oriented
inductive biases for textual reasoning tasks like document retrieval and
classification. All code for this case study is available at
https://github.com/HennyJie/GNN-DocRetrieval.

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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: econ