Combining Recurrent and Convolutional Neural Networks for Relation Classification

Vu, Ngoc Thang; Adel, Heike; Gupta, Pankaj; Schütze, Hinrich

doi:10.18653/v1/n16-1065

Cited by 139 publications

(94 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many deep learning models have been proposed for relation extraction, with a focus on end-to-end training using CNNs (Zeng et al, 2014;Nguyen and Grishman, 2015) and RNNs (Zhang et al, 2015). Other popular approaches include using CNN or RNN over dependency paths between entities (Xu et al, 2015a,b), augmenting RNNs with different components Zhou et al, 2016), and combining RNNs and CNNs (Vu et al, 2016;Wang et al, 2016). compares the performance of CNN models against traditional approaches on slot filling using a portion of the TAC KBP evaluation data.…”

Section: Related Workmentioning

confidence: 99%

Position-aware Attention and Supervised Data Improve Slot Filling

Zhang¹,

Zhong²,

Chen³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

568

668

View full text Add to dashboard Cite

Organized relational knowledge in the form of "knowledge graphs" is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset, obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F 1 score increases markedly from 22.2% to 26.7%.

show abstract

Section: Related Workmentioning

confidence: 99%

Position-aware Attention and Supervised Data Improve Slot Filling

Zhang¹,

Zhong²,

Chen³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

568

668

View full text Add to dashboard Cite

show abstract

“…Neural network classifiers are popular for relation extraction recently. Many of them focus on fully supervised settings, recurrent neural networks (RNN) and convolutional neural networks (CNN) (Vu et al, 2016;Zeng et al, 2015;Xu et al, 2015a;Xu et al, 2015b;Zhang and Wang, 2015), sequence models and tree models are investigated (Li et al, 2015;dos Santos et al, 2015). One similar network structure to our model is proposed in (Miwa and Bansal, 2016).…”

Section: Related Workmentioning

confidence: 99%

Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network

Sun

Lan

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

We investigate the task of open domain opinion relation extraction. Given a large number of unlabelled texts, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is designed to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset, and show that the proposed model is able to achieve promising performances without any human annotations.

show abstract

“…Patterns in larger text fragments can be encoded and exploited by recurrent (RNNs) or convolutional neural networks (CNNs) which have been successfully used for various sentence-based classification tasks, e.g. sentiment (Kim, 2014) or relation classification (Vu et al, 2016;Tai et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention

Becker

Staniek

Năstase

et al. 2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

View full text Add to dashboard Cite

Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.

show abstract

Combining Recurrent and Convolutional Neural Networks for Relation Classification

Cited by 139 publications

References 5 publications

Position-aware Attention and Supervised Data Improve Slot Filling

Position-aware Attention and Supervised Data Improve Slot Filling

Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network

Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention

Contact Info

Product

Resources

About