Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909078973841408 |
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| author | Jain, Monika Mutharaju, Raghava Kavuluru, Ramakanth Singh, Kuldeep |
| author_facet | Jain, Monika Mutharaju, Raghava Kavuluru, Ramakanth Singh, Kuldeep |
| contents | Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a knowledge graph with distinct benefits: 1) Our approach combines entity context with document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on three benchmark datasets: DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based link prediction techniques can enhance the performance of document-level relation extraction models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction Jain, Monika Mutharaju, Raghava Kavuluru, Ramakanth Singh, Kuldeep Information Retrieval Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a knowledge graph with distinct benefits: 1) Our approach combines entity context with document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on three benchmark datasets: DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based link prediction techniques can enhance the performance of document-level relation extraction models. |
| title | Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2401.11800 |