A Simple but Effective Bidirectional Extraction Framework for Relational
Triple Extraction
- URL: http://arxiv.org/abs/2112.04940v1
- Date: Thu, 9 Dec 2021 14:17:33 GMT
- Title: A Simple but Effective Bidirectional Extraction Framework for Relational
Triple Extraction
- Authors: Feiliang Ren, Longhui Zhang, Xiaofeng Zhao, Shujuan Yin, Shilei Liu,
Bochao Li
- Abstract summary: Tagging based relational triple extraction methods are attracting growing research attention recently.
Most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted.
This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects.
We propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions.
- Score: 0.9926500244448218
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Tagging based relational triple extraction methods are attracting growing
research attention recently. However, most of these methods take a
unidirectional extraction framework that first extracts all subjects and then
extracts objects and relations simultaneously based on the subjects extracted.
This framework has an obvious deficiency that it is too sensitive to the
extraction results of subjects. To overcome this deficiency, we propose a
bidirectional extraction framework based method that extracts triples based on
the entity pairs extracted from two complementary directions. Concretely, we
first extract all possible subject-object pairs from two paralleled directions.
These two extraction directions are connected by a shared encoder component,
thus the extraction features from one direction can flow to another direction
and vice versa. By this way, the extractions of two directions can boost and
complement each other. Next, we assign all possible relations for each entity
pair by a biaffine model. During training, we observe that the share structure
will lead to a convergence rate inconsistency issue which is harmful to
performance. So we propose a share-aware learning mechanism to address it. We
evaluate the proposed model on multiple benchmark datasets. Extensive
experimental results show that the proposed model is very effective and it
achieves state-of-the-art results on all of these datasets. Moreover,
experiments show that both the proposed bidirectional extraction framework and
the share-aware learning mechanism have good adaptability and can be used to
improve the performance of other tagging based methods. The source code of our
work is available at: https://github.com/neukg/BiRTE.
Related papers
- Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect
Sentiment Triplet Extraction [63.0205418944714]
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.
Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table.
We propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information.
arXiv Detail & Related papers (2023-12-18T12:46:09Z) - BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based
Joint Relational Triple Extraction Framework [16.930809038479666]
We propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework.
Specifically, we design a supervised contrastive learning method that considers multiple positives per anchor rather than restricting it to just one positive.
Our framework implements taggers in two directions, enabling triples extraction from subject to object and object to subject.
arXiv Detail & Related papers (2023-09-21T07:55:54Z) - CARE: Co-Attention Network for Joint Entity and Relation Extraction [0.0]
We propose a Co-Attention network for joint entity and relation extraction.
Our approach includes adopting a parallel encoding strategy to learn separate representations for each subtask.
At the core of our approach is the co-attention module that captures two-way interaction between the two subtasks.
arXiv Detail & Related papers (2023-08-24T03:40:54Z) - HIORE: Leveraging High-order Interactions for Unified Entity Relation
Extraction [85.80317530027212]
We propose HIORE, a new method for unified entity relation extraction.
The key insight is to leverage the complex association among word pairs, which contains richer information than the first-order word-by-word interactions.
Experiments show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.11.8 F1 points over the prior best unified model.
arXiv Detail & Related papers (2023-05-07T14:57:42Z) - ReSel: N-ary Relation Extraction from Scientific Text and Tables by
Learning to Retrieve and Select [53.071352033539526]
We study the problem of extracting N-ary relations from scientific articles.
Our proposed method ReSel decomposes this task into a two-stage procedure.
Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
arXiv Detail & Related papers (2022-10-26T02:28:02Z) - 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) - D-REX: Dialogue Relation Extraction with Explanations [65.3862263565638]
This work focuses on extracting explanations that indicate that a relation exists while using only partially labeled data.
We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations.
We find that about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model.
arXiv Detail & Related papers (2021-09-10T22:30:48Z) - Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot
Relational Triple Extraction [40.00702385889112]
We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples.
We design a hybrid learning mechanism that bridges text and knowledge concerning both entities and relations.
Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
arXiv Detail & Related papers (2020-10-30T04:18:39Z) - 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) - Position-Aware Tagging for Aspect Sentiment Triplet Extraction [37.76744150888183]
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets.
We propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets.
arXiv Detail & Related papers (2020-10-06T10:40:34Z) - Relabel the Noise: Joint Extraction of Entities and Relations via
Cooperative Multiagents [52.55119217982361]
We propose a joint extraction approach to handle noisy instances with a group of cooperative multiagents.
To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective.
A confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels.
arXiv Detail & Related papers (2020-04-21T12:03:04Z)
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.