A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction

• URL: http://arxiv.org/abs/2106.14163v2
• Date: Thu, 23 May 2024 11:56:15 GMT
• Title: A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction
• Authors: Jian Cheng, Tian Zhang, Shuang Zhang, Huimin Ren, Guo Yu, Xiliang Zhang, Shangce Gao, Lianbo Ma,
• Abstract summary: We propose an effective cascade dual-decoder method to extract overlapping relational triples. Our approach is straightforward and it includes a text-specific relation decoder and a relation-corresponded entity decoder. We conducted experiments on a real-world open-pit mine dataset and two public datasets to verify the method's generalizability.
• Score: 18.66493402386152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In knowledge graph construction, a challenging issue is how to extract complex (e.g., overlapping) entities and relationships from a small amount of unstructured historical data. The traditional pipeline methods are to divide the extraction into two separate subtasks, which misses the potential interaction between the two subtasks and may lead to error propagation. In this work, we propose an effective cascade dual-decoder method to extract overlapping relational triples, which includes a text-specific relation decoder and a relation-corresponded entity decoder. Our approach is straightforward and it includes a text-specific relation decoder and a relation-corresponded entity decoder. The text-specific relation decoder detects relations from a sentence at the text level. That is, it does this according to the semantic information of the whole sentence. For each extracted relation, which is with trainable embedding, the relation-corresponded entity decoder detects the corresponding head and tail entities using a span-based tagging scheme. In this way, the overlapping triple problem can be tackled naturally. We conducted experiments on a real-world open-pit mine dataset and two public datasets to verify the method's generalizability. The experimental results demonstrate the effectiveness and competitiveness of our proposed method and achieve better F1 scores under strict evaluation metrics. Our implementation is available at https://github.com/prastunlp/DualDec.

Related papers

• Relation Rectification in Diffusion Model [64.84686527988809]
  We introduce a novel task termed Relation Rectification, aiming to refine the model to accurately represent a given relationship it initially fails to generate. We propose an innovative solution utilizing a Heterogeneous Graph Convolutional Network (HGCN) The lightweight HGCN adjusts the text embeddings generated by the text encoder, ensuring the accurate reflection of the textual relation in the embedding space.
  arXiv  Detail & Related papers  (2024-03-29T15:54:36Z)
• Mutually Guided Few-shot Learning for Relational Triple Extraction [10.539566491939844]
  Mutually Guided Few-shot learning framework for Triple Extraction (MG-FTE) Our method consists of an entity-guided relation-decoder to classify relations and a proto-decoder to extract entities. Our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single domain) and 20.5 F1 score on FewRel 2.0 (cross-domain)
  arXiv  Detail & Related papers  (2023-06-23T06:15:54Z)
• Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
  We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings. RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
  arXiv  Detail & Related papers  (2022-12-17T05:25:17Z)
• UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction [29.15806644012706]
  We propose UniRel to address the challenges of capturing rich correlations between entities and relations. Specifically, we unify representations of entities and relations by jointly encoding them within a relationald natural language sequence. With comprehensive experiments on two popular triple extraction datasets, we demonstrate that UniRel is more effective computationally efficient.
  arXiv  Detail & Related papers  (2022-11-16T16:53:13Z)
• RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction [65.4337085607711]
  We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) Given an input sentence, each extracted triplet consists of the head entity, relation label, and tail entity where the relation label is not seen at the training stage. We propose to synthesize relation examples by prompting language models to generate structured texts.
  arXiv  Detail & Related papers  (2022-03-17T05:55:14Z)
• OneRel:Joint Entity and Relation Extraction with One Module in One Step [42.576188878294886]
  Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. We propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem.
  arXiv  Detail & Related papers  (2022-03-10T15:09:59Z)
• A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction [5.059120569845976]
  We present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. We build a memory module to remember category representations learned in entity recognition and relation extraction tasks. We also design a multi-level memory flow attention mechanism to enhance the bi-directional interaction between entity recognition and relation extraction.
  arXiv  Detail & Related papers  (2021-01-25T16:24:04Z)
• Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction [84.64435075778988]
  We propose a general approach to learn relation prototypes from unlabeled texts. We learn relation prototypes as an implicit factor between entities. We conduct experiments on two publicly available datasets: New York Times and Google Distant Supervision.
  arXiv  Detail & Related papers  (2020-11-27T06:21:12Z)
• Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks [61.950353376870154]
  Joint-event-extraction is a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. We propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of triggers or entities. Our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
  arXiv  Detail & Related papers  (2020-10-13T11:51:17Z)
• HittER: Hierarchical Transformers for Knowledge Graph Embeddings [85.93509934018499]
  We propose Hitt to learn representations of entities and relations in a complex knowledge graph. Experimental results show that Hitt achieves new state-of-the-art results on multiple link prediction. We additionally propose a simple approach to integrate Hitt into BERT and demonstrate its effectiveness on two Freebase factoid answering datasets.
  arXiv  Detail & Related papers  (2020-08-28T18:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.