Yu-Ming Shang
Wei Wei
Skip Abstract Section
Abstract
Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Previous studies usually obtain this property by greedily learning the local connections between relations. However, they are essentially limited because of failing to capture the global topology structure of relation ties and may easily fall into a locally optimal solution. To address this issue, we propose a novel force-directed graph to comprehensively learn relation ties. Specifically, we first construct a graph according to the global co-occurrence of all relations. Then, we borrow the idea of Coulomb’s law from physics and introduce the concept of attractive force and repulsive force into this graph to learn correlation and mutual exclusion between relations. Finally, the obtained relation representations are applied as an inter-dependent relation classifier. Extensive experimental results demonstrate that our method is capable of modeling global correlation and mutual exclusion between relations, and outperforms the state-of-the-art baselines. In addition, the proposed force-directed graph can be used as a module to augment existing relation extraction systems and improve their performance.
- [1] . 2019. Fine-tuning pre-trained transformer language models to distantly supervised relation extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1388–1398.Google Scholar
Cross Ref
- [2] . 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13 (Feb. 2012), 281–305.Google Scholar
- [3] . 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. ACM, New York, NY, 1247–1250.Google ScholarDigital Library
- [4] . 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems 26. Curran Associates, 2787–2795.Google ScholarDigital Library
- [5] . 2004. Dependency tree kernels for relation extraction. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. 423.Google ScholarDigital Library
- [6] . 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers). 4171–4186.Google Scholar
- [7] . 2018. Reinforcement learning for relation classification from noisy data. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 5779–5786.Google ScholarCross Ref
- [8] . 2017. Effective deep memory networks for distant supervised relation extraction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 4002–4008.Google ScholarDigital Library
- [9] . 2020. A dynamic parameter enhanced network for distant supervised relation extraction. Knowledge-Based Systems 197 (2020), 105912.Google ScholarCross Ref
- [10] . 2005. Exploring various knowledge in relation extraction. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. 427–434.Google ScholarDigital Library
- [11] . 2013. Fundamentals of Physics. John Wiley & Sons.Google Scholar
- [12] . 2016. Global distant supervision for relation extraction. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2950–2956.Google ScholarCross Ref
- [13] . 2011. Knowledge-based weak supervision for information extraction of overlapping relations. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 1). 541–550.Google ScholarDigital Library
- [14] . 2011. A new term ranking method based on relation extraction and graph model for text classification. In Proceedings of the 34th Australasian Computer Science Conference, Vol. 113. 145–152.Google Scholar
- [15] . 2018. Improving distantly supervised relation extraction using word and entity based attention. CoRR abs/1804.06987 (2018). http://arxiv.org/abs/1804.06987.Google Scholar
- [16] . 2017. Distant supervision for relation extraction with sentence-level attention and entity descriptions. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17). 3060–3066.Google ScholarCross Ref
- [17] . 2016. Relation extraction with multi-instance multi-label convolutional neural networks. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers (COLING’16). 1471–1480.Google Scholar
- [18] . 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).Google Scholar
- [19] . 2018. Cooperative denoising for distantly supervised relation extraction. In Proceedings of the 27th International Conference on Computational Linguistics. 426–436.Google Scholar
- [20] . 2020. A comprehensive exploration of semantic relation extraction via pre-trained CNNs. Knowledge-Based Systems 194 (2020), 105488.Google ScholarCross Ref
- [21] . 2020. Self-attention enhanced selective gate with entity-aware embedding for distantly supervised relation extraction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 8269–8276.Google ScholarCross Ref
- [22] . 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2181–2187.Google ScholarCross Ref
- [23] . 2016. Neural relation extraction with selective attention over instances. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2124–2133.Google ScholarCross Ref
- [24] . 2014. Exploring fine-grained entity type constraints for distantly supervised relation extraction. In Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers (COLING’14). 2107–2116.Google Scholar
- [25] . 2010. Combining relations for information extraction from free text. ACM Transactions on Information Systems 28, 3 (July 2010), Article 14, 35 pages. Google ScholarDigital Library
- [26] . 2009. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (Volume 2). 1003–1011.Google ScholarCross Ref
- [27] . 2018. Robust distant supervision relation extraction via deep reinforcement learning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2137–2147.Google ScholarCross Ref
- [28] . 2018. Improving Language Understanding by Generative Pre-training. Retrieved March 18, 2022 from https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.Google Scholar
- [29] . 2018. Sentence relations for extractive summarization with deep neural networks. ACM Transactions on Information Systems 36, 4 (April 2018), Article 39, 32 pages. Google ScholarDigital Library
- [30] . 2010. Modeling relations and their mentions without labeled text. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 148–163.Google ScholarDigital Library
- [31] . 1994. Information extraction as a basis for high-precision text classification. ACM Transactions on Information Systems 12, 3 (
July 1994), 296–333. Google ScholarDigital Library - [32] . 2015. VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 1456–1464.Google ScholarCross Ref
- [33] . 2016. Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 1715–1725.Google ScholarCross Ref
- [34] . 2020. Are noisy sentences useless for distant supervised relation extraction? In Proceedings of the AAAI Conference on Artificial Intelligence. 8799–8806.Google ScholarCross Ref
- [35] . 2018. Exploring encoder-decoder model for distant supervised relation extraction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 4389–4395.Google ScholarDigital Library
- [36] . 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319–2323.Google ScholarCross Ref
- [37] . 2018. RESIDE: Improving distantly-supervised neural relation extraction using side information. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 1257–1266.Google ScholarCross Ref
- [38] . 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30. Curran Associates, 5998–6008.Google Scholar
- [39] . 2018. Label-free distant supervision for relation extraction via knowledge graph embedding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2246–2255.Google ScholarCross Ref
- [40] . 2017. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29, 12 (2017), 2724–2743.Google ScholarCross Ref
- [41] . 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. 1112–1119.Google ScholarCross Ref
- [42] . 2009. Unsupervised relation extraction by mining Wikipedia texts using information from the web. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (Volume 2). 1021–1029.Google ScholarCross Ref
- [43] . 2017. Jointly extracting relations with class ties via effective deep ranking. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.Google ScholarCross Ref
- [44] . 2019. Distant supervision relation extraction with intra-bag and inter-bag attentions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers). 2810–2819.Google ScholarCross Ref
- [45] . 2020. ToHRE: A top-down classification strategy with hierarchical bag representation for distantly supervised relation extraction. In Proceedings of the 28th International Conference on Computational Linguistics. 1665–1676.Google ScholarCross Ref
- [46] . 2019. Distant supervision for relation extraction with linear attenuation simulation and non-IID relevance embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 7418–7425.Google ScholarDigital Library
- [47] . 2019. Cross-relation cross-bag attention for distantly-supervised relation extraction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 419–426.Google ScholarDigital Library
- [48] . 2015. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1753–1762.Google ScholarCross Ref
- [49] . 2014. Relation classification via convolutional deep neural network. In Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers (COLING’14). 2335–2344.Google Scholar
- [50] . 2019. Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers). 3016–3025.Google ScholarCross Ref
- [51] . 2019. Question answering in knowledge bases: A verification assisted model with iterative training. ACM Transactions on Information Systems 37, 4 (Sept. 2019), Article 40, 26 pages. Google ScholarDigital Library
- [52] . 2020. CFSRE: Context-aware based on frame-semantics for distantly supervised relation extraction. Knowledge-Based Systems 210 (2020), 106480.Google ScholarCross Ref
- [53] . 2019. Knowledge-enriched transformer for emotion detection in textual conversations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). 165–176.Google ScholarCross Ref
Index Terms
- Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
Recommendations
A Long-Tail Relation Extraction Model Based on Dependency Path and Relation Graph Embedding
Web and Big DataAbstractDistant supervision, a method for relation extraction, leverages knowledge base triples to label entities and relations in text, but this leads to noisy labels and long-tail problems. Among long-tail dependency structures, the hierarchy tree of ...
Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction
The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices ...
ArabRelat: Arabic Relation Extraction using Distant Supervision
IC3K 2015: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge ManagementRelation Extraction is an important preprocessing task for a number of text mining applications, including:
Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a
novel Arabic relation extraction method ...
Comments