A triple joint extraction method combining hybrid embedding and relational label embedding
The purpose of triple extraction is to obtain relationships between entities from unstructured text and apply them to downstream tasks.The embedding mechanism has a great impact on the performance of the triple extraction model, and the embedding vector should contain rich semantic information that...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-02-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023021/ |
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| author | Jianfeng DAI Xingyu CHEN Ligang DONG Xian JIANG |
| author_facet | Jianfeng DAI Xingyu CHEN Ligang DONG Xian JIANG |
| author_sort | Jianfeng DAI |
| collection | DOAJ |
| description | The purpose of triple extraction is to obtain relationships between entities from unstructured text and apply them to downstream tasks.The embedding mechanism has a great impact on the performance of the triple extraction model, and the embedding vector should contain rich semantic information that is closely related to the relationship extraction task.In Chinese datasets, the information contained between words is very different, and in order to avoid the loss of semantic information problems generated by word separation errors, a triple joint extraction method combining hybrid embedding and relational label embedding (HEPA) was designed, and a hybrid embedding means that combines letter embedding and word embedding was proposed to reduce the errors generated by word separation errors.A relational embedding mechanism that fuses text and relational labels was added, and an attention mechanism was used to distinguish the relevance of entities in a sentence with different relational labels, thus improving the matching accuracy.The method of matching entities with pointer annotation was used, which improved the extraction effect on relational overlapping triples.Comparative experiments are conducted on the publicly available DuIE dataset, and the F1 value of HEPA is improved by 2.8% compared to the best performing baseline model (CasRel). |
| format | Article |
| id | doaj-art-046ffbf3ab0749c58974ac19ead72ca9 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-046ffbf3ab0749c58974ac19ead72ca92025-01-15T02:59:06ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-02-013913214459570845A triple joint extraction method combining hybrid embedding and relational label embeddingJianfeng DAIXingyu CHENLigang DONGXian JIANGThe purpose of triple extraction is to obtain relationships between entities from unstructured text and apply them to downstream tasks.The embedding mechanism has a great impact on the performance of the triple extraction model, and the embedding vector should contain rich semantic information that is closely related to the relationship extraction task.In Chinese datasets, the information contained between words is very different, and in order to avoid the loss of semantic information problems generated by word separation errors, a triple joint extraction method combining hybrid embedding and relational label embedding (HEPA) was designed, and a hybrid embedding means that combines letter embedding and word embedding was proposed to reduce the errors generated by word separation errors.A relational embedding mechanism that fuses text and relational labels was added, and an attention mechanism was used to distinguish the relevance of entities in a sentence with different relational labels, thus improving the matching accuracy.The method of matching entities with pointer annotation was used, which improved the extraction effect on relational overlapping triples.Comparative experiments are conducted on the publicly available DuIE dataset, and the F1 value of HEPA is improved by 2.8% compared to the best performing baseline model (CasRel).http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023021/triple extractionrelational embeddingBERTattention mechanismpointer annotation |
| spellingShingle | Jianfeng DAI Xingyu CHEN Ligang DONG Xian JIANG A triple joint extraction method combining hybrid embedding and relational label embedding Dianxin kexue triple extraction relational embedding BERT attention mechanism pointer annotation |
| title | A triple joint extraction method combining hybrid embedding and relational label embedding |
| title_full | A triple joint extraction method combining hybrid embedding and relational label embedding |
| title_fullStr | A triple joint extraction method combining hybrid embedding and relational label embedding |
| title_full_unstemmed | A triple joint extraction method combining hybrid embedding and relational label embedding |
| title_short | A triple joint extraction method combining hybrid embedding and relational label embedding |
| title_sort | triple joint extraction method combining hybrid embedding and relational label embedding |
| topic | triple extraction relational embedding BERT attention mechanism pointer annotation |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023021/ |
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