End-to-End Neural Relation Extraction with Global Optimization

Zhang, Meishan; Zhang, Yue; Fu, Guohong

doi:10.18653/v1/d17-1182

Cited by 153 publications

(133 citation statements)

References 42 publications

Supporting

Mentioning

132

Contrasting

Unclassified

Order By: Relevance

“…Many state-of-the-art RE models rely upon domain-specific external syntactic tools to construct dependency paths between the entities in a sentence (Li and Ji, 2014;Xu et al, 2015;Miwa and Bansal, 2016;Zhang et al, 2017). These systems suffer from cascading errors from these tools and are hard to generalize to different domains.…”

Section: Related Workmentioning

confidence: 99%

“…Bekoulis et al (2018) use adversarial training as regularization for a neural model. Zhang et al (2017) cast joint entity and relation extraction as a table filling problem and build a globally optimized neural model incorporating syntactic representations from a dependency parser. Similar to DYGIE, Sanh et al (2019) and Luan et al (2018a) use a multi-task learning framework for extracting entity, relation and coreference labels.…”

Section: Baselinesmentioning

confidence: 99%

See 1 more Smart Citation

A general framework for information extraction using dynamic span graphs

Luan¹,

Wadden²,

He³

et al. 2019

Proceedings of the 2019 Conference of the North

233

175

View full text Add to dashboard Cite

We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are constructed by selecting the most confident entity spans and linking these nodes with confidenceweighted relation types and coreferences. The dynamic span graph allows coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multitask frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms the state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset. 1

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Baselinesmentioning

confidence: 99%

A general framework for information extraction using dynamic span graphs

Luan¹,

Wadden²,

He³

et al. 2019

Proceedings of the 2019 Conference of the North

233

175

View full text Add to dashboard Cite

show abstract

“…In later studies, Li and Ji (2014) extract entity mentions and relations using structured perceptron with efficient beamsearch, which is significantly more efficient and less Time-consuming than constraint-based approaches. Miwa and Sasaki (2014); Gupta et al (2016); Zhang et al (2017) proposed the tablefilling approach, which provides an opportunity to incorporating more sophisticated features and algorithms into the model, such as search orders in decoding and global features. Neural network models have been widely used in the literature as well.…”

Section: Extracting Entities and Relationsmentioning

confidence: 99%

Entity-Relation Extraction as Multi-Turn Question Answering

Li¹,

Yin²,

Sun³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

266

115

View full text Add to dashboard Cite

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models.Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the stateof-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.4 (+1.0), 60.2 (+0.6) and 68.9 (+2.1), respectively.Additionally, we construct a newly developed dataset RESUME in Chinese, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset. 1 2

show abstract

“…Miwa and Bansal (2016) leverage LSTM architectures to jointly predict both entity and relations, but fall short on ensuring prediction consistency. Zhang et al (2017) combine the benefits of both neural net and global optimization with beam Figure 2: Deep neural network architecture for joint structured learning. Note that on the structured learning layer, grey bars denote tokens being predicted as events.…”

Section: Related Workmentioning

confidence: 99%

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Han¹,

Ning²,

Peng³

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

View full text Add to dashboard Cite

We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F 1 improved by 10% and 6.8% on two benchmark datasets respectively.

show abstract

End-to-End Neural Relation Extraction with Global Optimization

Cited by 153 publications

References 42 publications

A general framework for information extraction using dynamic span graphs

A general framework for information extraction using dynamic span graphs

Entity-Relation Extraction as Multi-Turn Question Answering

Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Contact Info

Product

Resources

About