Volume 16, Issue 1 (5-2019)                   JSDP 2019, 16(1): 3-20 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Reshadat V, HoorAli M, Faili H. A New Method for Improving Computational Cost of Open Information Extraction Systems Using Log-Linear Model. JSDP 2019; 16 (1) :3-20
URL: http://jsdp.rcisp.ac.ir/article-1-681-en.html
Miyaneh Technical and Engineering Faculty, University of Tabriz
Abstract:   (3490 Views)
Information extraction (IE) is a process of automatically providing a structured representation from an unstructured or semi-structured text. It is a long-standing challenge in natural language processing (NLP) which has been intensified by the increased volume of information and heterogeneity, and non-structured form of it. One of the core information extraction tasks is relation extraction which aims at extracting semantic relations among entities from natural language text. Traditional relation extraction techniques were relation-specific, producing new instances of relations determined a priori. While effective, this model is not applicable in cases where the relations are not defined a priori or when the number of relations is high. Open Relation Extraction (ORE) methods were developed to elicit instances of arbitrary relations while requiring fewer training examples. Since ORE systems are employed by the applications depended on large-scale relation extraction, high performance and low computational cost are major requirements for ORE methods. This is particularly important in the large scales such as the Web. Many OIE systems have been proposed in recent years. These approaches range from shallow (such as part-of-speech tagging) to deep (such as semantic role labeling), therefore they differ in their performance level and computational cost.
In this paper, we use the state-of-the-art shallow NLP tools to extract instances of relations. A supervised log-linear model for OIE is presented which is based on using advantages of shallow NLP tools, as they are fast and lead to a low computational time. Extractor which is the main core of proposed approach integrates a high performance subset of the shallow NLP tools with the strength of the deep NLP tools by using a supervised log linear model and produces a high performance method that is scalable. This causes efficient use of time and therefore reduces computational cost and increases precision. Proposed approach achieves higher precision and recall than ReVerb, one of the most successful shallow OIE system.
Full-Text [PDF 6285 kb]   (1904 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2017/11/9 | Accepted: 2019/01/26 | Published: 2019/06/10 | ePublished: 2019/06/10

References
1. [1] V. Reshadat, M. Hoorali, and H. Faili, "A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis," Inter-disciplinary Information Sciences, vol. 22, pp. 87-100, 2016. [DOI:10.4036/iis.2016.R.03]
2. [2] J. Piskorski and R. Yangarber, "Information extraction: Past, present and future," in Multi-source, Multilingual Information Extraction and Summarization, ed: Springer, 2013, pp. 23-49. [DOI:10.1007/978-3-642-28569-1_2]
3. [3] N. mollaei, A. Abdolahzadeh, H. A. Shirazi, new approach to extract the required information from military documents. JSDP. 2012; 9 (1): pp.67-80
4. [4] L. Del Corro and R. Gemulla, "ClausIE: clause-based open information extraction," in Procee-dings of the 22nd international conference on World Wide Web, 2013, pp. 355-366. [DOI:10.1145/2488388.2488420]
5. [5] O. Etzioni, M. Banko, S. Soderland, and D. S. Weld, "Open information extraction from the web," Communications of the ACM, vol. 51, pp. 68-74, 2008. [DOI:10.1145/1409360.1409378]
6. [6] O. Etzioni, A. Fader, J. Christensen, S. Soderland, and M. Mausam, "Open Information Extraction: The Second Generation," in IJCAI, 2011, pp. 3-10.
7. [7] F. Wu and D. S. Weld, "Open information extraction using Wikipedia," in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 118-127.
8. [8] A. Akbik and J. Broß, "Wanderlust: Extracting semantic relations from natural language text using dependency grammar patterns," in WWW Workshop, 2009.
9. [9] A. Akbik ,and A. Löser, "Kraken: N-ary facts in open information extraction," in Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, 2012, pp. 52-56.
10. [10] P. Gamallo, M. Garcia, and S. Fernández-Lanza, "Dependency-based open information extraction," in Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, 2012, pp. 10-18.
11. [11] V. Tablan, K. Bontcheva, D. Maynard, and H. Cunningham, "Ollie: on-line learning for information extraction," in Proceedings of the HLT-NAACL 2003 workshop on Software engi-neering and architecture of language techno-logy systems-Volume 8, 2003, pp. 17-24. [DOI:10.3115/1119226.1119229]
12. [12] A. Fader, S. Soderland, and O. Etzioni, "Identify-ing relations for open information extraction," in Proceedings of the Conference on Empirical Methods in Natural Language Process-ing, 2011, pp. 1535-1545.
13. [13] F. Mesquita, J. Schmidek, and D. Barbosa, "Effectiveness and efficiency of open relation ex-traction," in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, vol. 500, pp. 447-457, 2013.
14. [14] M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, "Open information extraction for the web," in IJCAI, 2007, pp. 2670-2676.
15. [15] Y. Merhav, F. Mesquita, D. Barbosa, W. G. Yee, and O. Frieder, "Extracting information networks from the blogosphere," ACM Transactions on the Web (TWEB), vol. 6, p. 11, 2012. [DOI:10.1145/2344416.2344418]
16. [16] L. Qiu and Y. Zhang, "Zore: A syntax-based system for chinese open relation extraction," in Proceedings of EMNLP, 2014. [DOI:10.3115/v1/D14-1201] [PMID]
17. [17] Y.-H. Tseng, L.-H. Lee, S.-Y. Lin, B.-S. Liao, M.-J. Liu, H.-H. Chen, O. Etzioni, and A. Fader, "Chinese open relation extraction for knowledge acquisition," EACL 2014, p. 12, 2014. [DOI:10.3115/v1/E14-4003] [PMCID]
18. [18] P. Gamallo and M. Garcia, "Multilingual open information extraction," in Portuguese Con-ference on Artificial Intelligence, 2015, pp. 711-722. [DOI:10.1007/978-3-319-23485-4_72]
19. [19] C. Castella Xavier, S. de Lima, V. Lúcia, and M. Souza, "Open information extraction based on lexical-syntactic patterns," in Intelligent Systems (BRACIS), 2013 Brazilian Conference on, 2013, pp. 189-194. [DOI:10.1109/BRACIS.2013.39]
20. [20] P. Cimiano ,and J. Wenderoth, "Automatically learning qualia structures from the web," in Proceedings of the ACL-SIGLEX workshop on deep lexical acquisition, 2005, pp. 28-37. [DOI:10.3115/1631850.1631854]
21. [21] M. Schmitz, R. Bart, S. Soderland, and O. Etzioni, "Open language learning for information extraction," in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 523-534.
22. [22] N. Nakashole, G. Weikum, and F. Suchanek, "PATTY: a taxonomy of relational patterns with semantic types," in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 1135-1145.
23. [23] H. Bast and E. Haussmann, "Open information extraction via contextual sentence decomposi-tion," in Semantic Computing (ICSC), 2013 IEEE Seventh International Conference on, 2013, pp. 154-159. [DOI:10.1109/ICSC.2013.36]
24. [24] H. Bast and E. Haussmann, "More informative open information extraction via simple inference," in Advances in information retrieval, ed: Springer, 2014, pp. 585-590. [DOI:10.1007/978-3-319-06028-6_61]
25. [25] H. Lin, Y. Wang, P. Zhang, W. Wang, Y. Yue, and Z. Lin, "A Rule Based Open Information Extraction Method Using Cascaded Finite-State Transducer," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2016, pp. 325-337. [DOI:10.1007/978-3-319-31750-2_26]
26. [26] Y. Xu, M.-Y. Kim, K. Quinn, R. Goebel, and D. Barbosa, "Open Information Extraction with Tree Kernels," in HLT-NAACL, 2013, pp. 868-877.
27. [27] J. Christensen, S. Soderland, and O. Etzioni, "An analysis of open information extraction based on semantic role labeling," in Proceedings of the sixth international conference on Knowledge capture, 2011, pp. 113-120. [DOI:10.1145/1999676.1999697]
28. [28] V. Punyakanok, D. Roth, and W.-t. Yih, "The importance of syntactic parsing and inference in semantic role labeling," Computational Linguistics, vol. 34, pp. 257-287, 2008. [DOI:10.1162/coli.2008.34.2.257]
29. [29]R. Johansson and P. Nugues, "The effect of syntactic representation on semantic role labeling," in Proceedings

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing