Ekstraksi Relasi Meronymy dengan Lexico-Syntactic Patterns
DOI:
https://doi.org/10.26418/jp.v6i1.36549Keywords:
ekstraksi, Lexico-Syntactic Patterns, ontologi, meronymy, relasiAbstract
Ontologi terdiri atas konsep dan relasi yang masing-masing dapat diekstrak dengan berbagai macam metode. Salah satu metode yang dapat digunakan untuk ekstraksi relasi adalah metode berdasarkan Lexico-Syntactic Patterns. Secara sederhana, ekstraksi relasi dilakukan dengan mendapatkan sebuah pola yang menunjukkan sebuah relasi. Kemudian dilakukan percobaan untuk menguji apakah pola yang didapatkan mampu memprediksi relasi dengan tepat. Pada penelitian ini dilakukan percobaan untuk menguji pola relasi meronymy yang didapatkan dari dataset penelitian terdahulu. Evaluasi dilakukan dengan menggunakan nilai recall dan precision. Dari penelitian ini, ditemukan bahwa banyaknya (keragaman) variasi dalam sekumpulan pola yang menunjukkan suatu relasi dapat mempengaruhi kemampuan kumpulan pola tersebut untuk memprediksi relasi dengan tepat. Semakin banyak variasi pola dalam satu relasi, maka ketepatan prediksi cenderung menurun.References
K. Lakel and F. Bendella, "Dynamic Evaluation of Ontologies," Procedia Computer Science, vol. 73, pp. 16-23, 2015.
L. Zettlemoyer, "Relation Extraction," Department of Computer Science & Engineering, University of Washington, 2013.
R. Granada, R. Vieira, C. Trojahn and N. Aussenac-Gilles, "Evaluating the Complementarity of Taxonomic Relation Extraction Methods Across Different Languages," arXiv, 8 November 2018.
M. A. Hearst, "Automated discovery of wordnet relations," in WordNet: An electronic lexical database and some of its applications, 1998, p. 131–153.
M. A. Hearst, "Automatic acquisition of hyponyms from large text corpora," in Proceedings of the 14th conference on Computational linguistics - Volume 2, COLING ’92, Stroudsburg, PA, USA, 1992.
P. Pantel and M. Pennacchiotti, "Espresso: Leveraging generic patterns for automatically harvesting semantic relations," in Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, ACL-44, Stroudsburg, PA,, 2006.
S. P. Ponzetto and M. Strube, "Taxonomy induction based on a collaboratively built knowledge repository," Artificial Intelligence, vol. 175, no. 9, pp. 1737-1756, June 2011.
S. Cederberg and D. Widdows, "Using lsa and noun coordination information to improve the precision and recall of automatic hyponymy extraction," in Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003 - Volume 4, CoNLL-2003, Stroudsburg, PA, USA, 2003.
V.-T. Phi and Y. Matsumoto, "Integrating Word Embedding Offsets into the Espresso System for Part-Whole Relation Extraction," in Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers, Seoul, South Korea, 2016.
C. Klaussner and D. Zhekova, "Lexico-Syntactic Patterns for Automatic Ontology Building," in Proceedings of the Second Student Research Workshop associated with RANLP 2011, Hissar, Bulgaria, 2011.
D. M. W. Powers, "Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation," Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37-63, 2011.
W. R. v. Hage and G. Schreiber, "A Method for Learning Part-Whole Relations," in ISWC 2006: 5th International Semantic Web Conference, Athens, GA, USA, 2006.
V.-T. Phi, J. Santoso, M. Shimbo and Y. Matsumoto, "Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction," in 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018.