Extraction Method with Word Distribution Enriched Deep Residual Network
Authors: 
Chilong Wang, Zhixing Li, Shiya Ren, Huaming Wang, Feng Hu, Weibing DengAuthors Info & Claims
Pages 211 - 218
Abstract
As a core task and important part of information extraction, relation extraction identifies the semantic relation between entity pairs. It plays an important role in semantic understanding of sentences and the construction of knowledge graphs. Most of the existing methods for relation extraction rely on semantic information. Furthermore, many word embedding models do not take position information into considerations. In this paper, combining with word vector representation of word embedding and words' positions, a word distribution model is proposed. It is used as the input of Residual Neural Network to train the classifier for relation extraction and Adversarial Training method is employed to reduce the impact of noise labels in training phase. The experimental results demonstrate the effectiveness of the proposed model on several datasets.
References
[1]
Yang Z, He X, Gao J, et al. Stacked attention networks for image question answering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 21--29.
[2]
Auer S, Bizer C, Kobilarov G, et al. Dbpedia: A nucleus for a web of open data[M]//The semantic web. Springer, Berlin, Heidelberg, 2007: 722--735.
[3]
Bollacker K, Evans C, Paritosh P, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD international conference on Management of data. AcM, 2008: 1247--1250.
[4]
Vrandecic D, Krötzsch M. Wikidata: a free collaborative knowledgebase[J]. Communications of the ACM, 2014, 57(10): 78--85.
[5]
Suchanek F M, Kasneci G, Weikum G. Yago: a core of semantic knowledge[C]//Proceedings of the 16th international conference on World Wide Web. ACM, 2007: 697--706.
[6]
Hoffmann R, Zhang C, Weld D S. Learning 5000 relational extractors[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010: 286--295.
[7]
Mintz M, Bills S, Snow R, et al. Distant supervision for relation extraction without labeled data[C]//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-Volume 2. Association for Computational Linguistics, 2009: 1003--1011.
[8]
Riedel S, Yao L, McCallum A. Modeling relations and their mentions without labeled text[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010: 148--163.
[9]
Surdeanu M, Tibshirani J, Nallapati R, et al. Multi-instance multi-label learning for relation extraction[C]//Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, 2012: 455--465.
[10]
Zeng D, Liu K, Lai S, et al. Relation Classification via Convolutional Deep Neural Network[C]//Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 2014: 2335--2344.
[11]
Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2016, 2: 207--212.
[12]
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770--778.
[13]
Zeng D, Liu K, Chen Y, et al. Distant supervision for relation extraction via piecewise convolutional neural networks[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015: 1753--1762.
[14]
Lin Y, Shen S, Liu Z, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2124--2133.
[15]
Ren S, Li Z, Wang H, et al. NEARM: Natural Language Enhanced Association Rules Mining[C]//2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018: 438--445.
[16]
Yang X, Ren S, Li Y, et al. Relation Linking for Wikidata Using Bag of Distribution Representation[C]//National CCF Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2017: 652--661.
[17]
Huang Y Y, Wang W Y. Deep Residual Learning for Weakly-Supervised Relation Extraction[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 1803--1807.
[18]
Miyato T, Dai A M, Goodfellow I. ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION[J]. stat, 2017: 1050: 6.
[19]
Wu Y, Bamman D, Russell S. Adversarial training for relation extraction[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 1778--1783.
[20]
Feng J, Huang M, Zhao L, et al. Reinforcement learning for relation classification from noisy data[C]//Thirty-Second AAAI Conference on Artificial Intelligence. 2018: 5779--5786.
Index Terms
- Extraction Method with Word Distribution Enriched Deep Residual Network
Recommendations
Two-Word Collocation Extraction Using Monolingual Word Alignment Method
Statistical bilingual word alignment has been well studied in the field of machine translation. This article adapts the bilingual word alignment algorithm into a monolingual scenario to extract collocations from monolingual corpus, based on the fact ...
Collocation extraction using monolingual word alignment method
EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2Statistical bilingual word alignment has been well studied in the context of machine translation. This paper adapts the bilingual word alignment algorithm to monolingual scenario to extract collocations from monolingual corpus. The monolingual corpus is ...
Experiments on pattern-based relation learning
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementRelation extraction is the task of extracting semantic relations - such as synonymy or hypernymy - between word pairs from corpus data. Past work in relation extraction has concentrated on manually creating templates to use in directly extracting word ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2019
232 pages
ISBN:9781450376754
DOI:10.1145/3373419
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Southwest Jiaotong University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 January 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICAIP 2019
ICAIP 2019: 2019 3rd International Conference on Advances in Image Processing
November 8 - 10, 2019
Chengdu, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 103Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in