Bidirectional Recurrent Convolutional Neural Network for Relation Classification

Cai, Rui; Zhang, Xiaodong; Wang, Houfeng

doi:10.18653/v1/p16-1072

Cited by 192 publications

(123 citation statements)

References 11 publications

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“…work focuses on sentence-level RE, i.e., extracting relational facts from a single sentence. In recent years, various neural models have been explored to encode relational patterns of entities for sentence-level RE, and achieve state-of-theart performance (Socher et al, 2012;Zeng et al, 2014Zeng et al, , 2015dos Santos et al, 2015;Xiao and Liu, 2016;Cai et al, 2016;Lin et al, 2016;Wu et al, 2017;Qin et al, 2018;Han et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

Yao

et al. 2019

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

374

417

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Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest humanannotated dataset for document-level RE from plain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. In order to verify the challenges of documentlevel RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED. Empirical results show that DocRED is challenging for existing RE methods, which indicates that document-level RE remains an open problem and requires further efforts. Based on the detailed analysis on the experiments, we discuss multiple promising directions for future research. We make DocRED and the code for our baselines publicly available at https: //github.com/thunlp/DocRED. * indicates equal contribution

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Section: Introductionmentioning

confidence: 99%

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

Yao

et al. 2019

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

374

417

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show abstract

“…Model F1 SVM (Rink and Harabagiu, 2010) 82.2 CNN (Zeng et al, 2014) 82.7 SDP-LSTM (Xu et al, 2015) 83.7 Att-BLSTM 84.0 BRCNN (Cai et al, 2016) 86.3 Ours 84.1 Xu et al (2015) achieved an F1-score of 83.7% via heterogeneous information along the SDP. BRCNN (Cai et al, 2016) combined CNN and two-channel LSTM units to learns features along SDP, and made use of POS tags, NER and WordNet hypernyms.…”

Section: Resultsmentioning

confidence: 99%

“…Santos et al (2015) proposed a similar model named CR-CNN, and replaced the cost function with a rankingbased function. Some models (Xu et al, 2015;Cai et al, 2016) leveraged the shortest dependency path(SDP) between two nominals. Others employed attention mechanism to capture more important semantic information.…”

Section: Related Workmentioning

confidence: 99%

Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text

Liang¹,

Xu²,

Zhao³

2017

Proceedings of the 2nd Workshop on Representation Learning for NLP

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Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words may carry important shortdistance semantic information. We propose a hybrid model, combining the merits of word-level and character-level representations to learn better representations on informal text. Experiments on two dataset of relation classification, SemEval-2010 Task8 and a large-scale one we compile from informal text, show that our model achieves a competitive result in the former and state-of-the-art with the other.

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“…Variants of convolutional networks include piecewise-CNN (PCNN) (Zeng et al, 2014), split CNN (Adel et al, 2016), CNN with sentencewise pooling (Jiang et al, 2016) and attention CNN . Recurrent neural networks (RNN) are another popular choice, and have been used in recent work in the form of recurrent CNNs (Cai et al, 2016) and attention RNNs (Zhou et al, 2016). An instance-level selective attention mechanism was introduced for MIML by , and has significantly improved the prediction accuracy for several of these base deep models.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Training for Relation Extraction

Wu¹,

Bamman²,

Russell³

2017

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

215

166

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Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.

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Bidirectional Recurrent Convolutional Neural Network for Relation Classification

Cited by 192 publications

References 11 publications

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

DocRED: A Large-Scale Document-Level Relation Extraction Dataset

Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text

Adversarial Training for Relation Extraction

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