The Solution for The PST-KDD-2024 OAG-Challenge
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914875257651200 |
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| author | Zhong, Shupeng Li, Xinger Jin, Shushan Yang, Yang |
| author_facet | Zhong, Shupeng Li, Xinger Jin, Shushan Yang, Yang |
| contents | In this paper, we introduce the second-place solution in the KDD-2024 OAG-Challenge paper source tracing track. Our solution is mainly based on two methods, BERT and GCN, and combines the reasoning results of BERT and GCN in the final submission to achieve complementary performance. In the BERT solution, we focus on processing the fragments that appear in the references of the paper, and use a variety of operations to reduce the redundant interference in the fragments, so that the information received by BERT is more refined. In the GCN solution, we map information such as paper fragments, abstracts, and titles to a high-dimensional semantic space through an embedding model, and try to build edges between titles, abstracts, and fragments to integrate contextual relationships for judgment. In the end, our solution achieved a remarkable score of 0.47691 in the competition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Solution for The PST-KDD-2024 OAG-Challenge Zhong, Shupeng Li, Xinger Jin, Shushan Yang, Yang Computation and Language Machine Learning In this paper, we introduce the second-place solution in the KDD-2024 OAG-Challenge paper source tracing track. Our solution is mainly based on two methods, BERT and GCN, and combines the reasoning results of BERT and GCN in the final submission to achieve complementary performance. In the BERT solution, we focus on processing the fragments that appear in the references of the paper, and use a variety of operations to reduce the redundant interference in the fragments, so that the information received by BERT is more refined. In the GCN solution, we map information such as paper fragments, abstracts, and titles to a high-dimensional semantic space through an embedding model, and try to build edges between titles, abstracts, and fragments to integrate contextual relationships for judgment. In the end, our solution achieved a remarkable score of 0.47691 in the competition. |
| title | The Solution for The PST-KDD-2024 OAG-Challenge |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.12827 |