HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866913899446534144 |
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| author | Liu, Shuliang Hu, Xuming Zhang, Chenwei Li, Shu`ang Wen, Lijie Yu, Philip S. |
| author_facet | Liu, Shuliang Hu, Xuming Zhang, Chenwei Li, Shu`ang Wen, Lijie Yu, Philip S. |
| contents | Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_02225 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction Liu, Shuliang Hu, Xuming Zhang, Chenwei Li, Shu`ang Wen, Lijie Yu, Philip S. Computation and Language Artificial Intelligence Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models. |
| title | HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2205.02225 |