Volume 19, Issue 1 (5-2022)                   JSDP 2022, 19(1): 101-110 | Back to browse issues page

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jafarabad M, Dianat R. Relation extraction based on word embedding with Crowdsourcing Process. JSDP. 2022; 19 (1) :101-110
URL: http://jsdp.rcisp.ac.ir/article-1-1074-en.html
University of Qom
Abstract:   (292 Views)
For data mining studies, due to the complexity of doing feature selection process in tasks by hand, we need to send some of labeling to the workers with crowdsourcing activities. The process of outsourcing data mining tasks to users is often handled by software systems without enough knowledge of the age or geography of the users' residence. We use convolutional neural network, for doing classification in six classes: USAGE, TOPIC, COMPARE, MODEL-FEATURE, RESULT and PART-WHOLE. This article extracts the data from the abstract of 450 scientific articles and it is a total of 835 relations. One hundred of these abstracts have been selected by the crowdsourcing. Classification results in this article have been done with a slight improvement in accuracy. In this study, we computed the classification results on a combination of vocabulary vectors with using of 450 abstract relation data (100 crowd source datasets with 350 standards). The results of the implementation of the classification algorithm give us performance improvement. This paper uses the population power to perform preparing data mining works. The proposed method by adding crowdsource data to the previous data was able to obtain better results rather than the top 5 methods.
Article number: 8
Full-Text [PDF 868 kb]   (80 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2019/09/27 | Accepted: 2020/11/21 | Published: 2022/06/22 | ePublished: 2022/06/22

References
1. [1] H. Rheingold, Smart mobs: The next social revolution. Basic books, 2007.
2. [2] D. Zhou, Q. Liu, J. C. Platt, C. Meek, & N. B. Shah, Regularized minimax conditional entropy for crowdsourcing. arXiv preprint arXiv:1503.07240, 2015.
3. [3] Y. Zhao, & Q. Zhu, "Evaluation on crowdsourcing research: Current status and future direction", Information Systems Frontiers, vol. 16(3), pp. 417-434, 2014. [DOI:10.1007/s10796-012-9350-4]
4. [4] S. Marjanovic, C. Fry, & J. Chataway, "Crowdsourcing based business models: In search of evidence for innovation 2.0", Science and public policy, vol. 39(3), pp. 318-332, 2012. [DOI:10.1093/scipol/scs009]
5. [5] J. Prpić, P. P.Shukla, J. H. Kietzmann, & I. P. McCarthy, "How to work a crowd: Developing crowd capital through crowdsourcing", Business Horizons, vol. 58(1), pp. 77-85, 2015. [DOI:10.1016/j.bushor.2014.09.005]
6. [6] J. Staiano and M. Guerini, "DepecheMood: a Lexicon for emotion analysis from crowd-annotated news," arXiv preprint arXiv1405, pp. 1605, 2014. [DOI:10.3115/v1/P14-2070]
7. [7] P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, "Comparing and combining sentiment analysis methods," in Proceedings of the first ACM conference on Online social networks, 2013, pp. 27-38. [DOI:10.1145/2512938.2512951]
8. [8] P. Belleflamme, T. Lambert, & A. Schwienbacher, "Crowdfunding: Tapping the right crowd", Journal of business venturing, vol. 29(5), pp. 585-609, 2014. [DOI:10.1016/j.jbusvent.2013.07.003]
9. [9] J. Daniels, & J. R. Feagin, "The (coming) social media revolution in the academy," Fast Capitalism, vol.8(2), 2019. [DOI:10.32855/fcapital.201102.001]
10. [10] T. A. Gautre, & T. H. Khan, "An analysis of question answering system for education empowered by crowdsourcing" In 2018 2nd International Conference on Inventive Systems and Control (ICISC), IEEE, 2018. [DOI:10.1109/ICISC.2018.8398942]
11. [11] K. Gábor, D. Buscaldi, A. K. Schumann, B. QasemiZadeh, H. Zargayouna, & T. Charnois, "Semeval-2018 Task 7: Semantic relation extraction and classification in scientific papers," In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 679-688, 2018. [DOI:10.18653/v1/S18-1111]
12. [12] M. Gluhak, M. P. di Buono, A. Akkasi, & J. Šnajder, "TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts", In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 842-847, 2018. [DOI:10.18653/v1/S18-1135]
13. [13] J. Rotsztejn, N. Hollenstein, & C. Zhang, Eth-ds3lab at semeval-2018 task 7: Effectively combining recurrent and convolutional neural networks for relation classification and extraction. arXiv preprint arXiv:1804.02042, 2018. [DOI:10.18653/v1/S18-1112]
14. [14] Y. Luan, M. Ostendorf, & H. Hajishirzi, "The uwnlp system at semeval-2018 task 7: Neural relation extraction model with selectively incorporated concept embeddings", In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 788-792, 2018, June. [DOI:10.18653/v1/S18-1125]
15. [15] S. A. Lazarus, Cyber Mobs: A Model for Improving Protections for Internet Users (Doctoral dissertation, Utica College), 2017.
16. [16] B. L. Bayus, "Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community", Management science, vol. 59(1), pp. 226-244, 2013. [DOI:10.1287/mnsc.1120.1599]
17. [17] R. W. Ouyang, M. Srivastava, A.Toniolo, & T. J. Norman, "Truth discovery in crowdsourced detection of spatial events", IEEE, 2016. [DOI:10.1109/TKDE.2015.2504928]
18. [18] B. Xiang, The psychological effects of participation in crowdsourcing on customer's willingness to pay and recommend a brand, 2016.
19. [19] M. A. Rashid, K. Deo, D. Prasad, K. Singh, S. Chand, & M. Assaf, TEduChain: A platform for crowdsourcing tertiary education fund using blockchain technology. arXiv preprint arXiv:1901.06327, 2019. [DOI:10.1017/S0269888920000326]
20. [20] P. Welinder, & P. Perona, "Online crowdsourcing: rating annotators and obtaining cost-effective labels", In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops pp. 25-32, IEEE, 2010. [DOI:10.1109/CVPRW.2010.5543189]
21. [21] G. M. Leung, & K. Leung, "Crowdsourcing data to mitigate epidemics", The Lancet Digital Health, vol. 2(4), e156-e157, 2020. [DOI:10.1016/S2589-7500(20)30055-8]
22. [22] A. Drutsa, V. Fedorova, D. Ustalov, O. Megorskaya, E. Zerminova, & D. Baidakova, "Crowdsourcing Practice for Efficient Data Labeling: Aggregation, Incremental Relabeling, and Pricing", In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2623-2627, 2020. [DOI:10.1145/3318464.3383127]
23. [23] F. Nooralahzadeh, & L. Øvrelid, "Syntactic dependency representations in neural relation classification", arXiv preprint arXiv:1805.11461, 2018. [DOI:10.18653/v1/W18-2907]
24. [24] L. Hettinger, A. Dallmann, A. Zehe, T. Niebler, & A. Hotho, "Claire at semeval-2018 task 7: Classification of relations using embeddings", In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 836-841, 2018. [DOI:10.18653/v1/S18-1134]
25. [25] B. Pratap, D. Shank, O. Ositelu, & B. Galbraith, "Talla at SemEval-2018 task 7: Hybrid loss optimization for relation classification using convolutional neural networks", In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 863-867, 2018. [DOI:10.18653/v1/S18-1139]
26. [26] D. Jin, F. Dernoncourt, E. Sergeeva, M. McDermott, & G. Chauhan, "MIT-MEDG at SemEval-2018 task 7: Semantic relation classification via convolution neural network", In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 798-804, 2018. [DOI:10.18653/v1/S18-1127]
27. [27] M. Gluhak, M. P. di Buono, A. Akkasi, & J. Šnajder, "TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts, In Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 842-847, 2018. [DOI:10.18653/v1/S18-1135]
28. [28] J. Pennington, R. Socher, & C. D. Manning, "Glove: Global vectors for word representation", In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) ,pp. 1532-1543, 2014. [DOI:10.3115/v1/D14-1162]

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