Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Gurevych, Iryna; Miyao, Yusuke

doi:10.18653/v1/p16-2

2016

DOI: 10.18653/v1/p16-2

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Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Iryna Gurevych

Yusuke Miyao

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2023

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Cited by 2 publications

References 187 publications

(331 reference statements)

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Joint entity and relation extraction based on specific-relation attention mechanism and global features

Ren

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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In natural language processing, the research on extracting triple relations from unstructured texts to construct knowledge graphs has been widely concerned, however, few existing methods can solve the problem of overlapping triples and pay little attention to relations interaction with sentences. Besides, because entities have multiple forms, most methods rely on contextual features during entity recognition, and these models often misrepresent global relations. These all affect the accuracy of triplet extraction. To address the above issues, this paper proposes a joint extraction model with specific relation embeddings as well as incorporating global relations. It is called SRGF. Specifically, the model first converts the text into a vector representation that combines contextual features and global feature vectors through a pre-trained model and a graph convolutional network GCN, and then extracts topics. So entity extraction can be enhanced. An attention network guided by subject and relation information is employed to learn improved sentence representations for identifying the related objects. By doing so, it becomes possible to capture and model the interactions among entities, relations, and sentences, thus leading to better solutions for the problem of overlapping triples. Experimental results on public datasets NYT and WebNLG demonstrate that SRGF outperforms previous methods in triplet extraction.

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Joint entity and relation extraction based on specific-relation attention mechanism and global features

Ren

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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Multi-turn automatic question answering based entity-relation extraction method for power technology standards

Wang

Jiang

et al. 2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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The goal of entity relation extraction is to extract entities and the relations between entities from unstructured texts. Most of the existing research approaches are oriented towards common entity labels in general-purpose domains (e.g., time, place, person, institution, etc.) and simple texts in specialized domains (texts consisting of single sentences with low knowledge density). The existence of long-distance dependencies of entity pairs (cross-sentence entity pairs) and the phenomenon of overlapping relations (different relations sharing the same entity) in complex texts are ignored. However, complex texts are common in practical applications, especially in professional fields such as power technology standards, where the knowledge density of texts is high and the phenomenon of entity-pair cross-sentence dependency is significant. To solve the above problems, this paper proposes a novel multi-hop automatic question-and-answer-based entity relation extraction method, which combines the current well-established machine reading comprehension framework with the automatic question construction mechanism proposed in this paper, and uses the a priori knowledge provided by the question as the extraction type guide, and uses the multi-hop question-and-answer mechanism to reason about the answer span of the question, effectively alleviating the phenomena of overlapping relations and entity dependence on crosssentences in complex texts. We conducted extensive comparative experiments on the power technology standard dataset self-constructed in this paper, and the results show that the MT-auQA model proposed in this paper achieves optimal performance

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Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Cited by 2 publications

References 187 publications

Joint entity and relation extraction based on specific-relation attention mechanism and global features

Joint entity and relation extraction based on specific-relation attention mechanism and global features

Multi-turn automatic question answering based entity-relation extraction method for power technology standards

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