Browse > Article
http://dx.doi.org/10.3745/KTSDE.2016.5.11.555

Korean Semantic Role Labeling Based on Suffix Structure Analysis and Machine Learning  

Seok, Miran (필아이티(주) IT사업본부)
Kim, Yu-Seop (한림대학교 융합소프트웨어학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.5, no.11, 2016 , pp. 555-562 More about this Journal
Abstract
Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.
Keywords
Semantic Role Labeling; Suffix Structure Analysis; Josa; Eomi; Machine Learning; Support Vector Machine; Conditional Random Fields;
Citations & Related Records
연도 인용수 순위
  • Reference
1 V. Punyakanok, D. Roth, and W. Yih,. "The Importance of Syntactic Parsing and Inference in Semantic Role Labeling," Computational Linguistics, Vol.34, No.2, pp.257-287, 2008.   DOI
2 L. Marquez, X. Carreras, K. C. Litkowski, and S. Stevenson, "Semantic Role Labeling: An Introduction to the Special Issue," Computational Linguistics, Vol.34, No.2, pp.145-159, 2008.   DOI
3 S. Pradhan, W. Ward, K. Hacioglu, J. H. Martin, and D. Jurafsky, "Semantic Parsing using Support Vector Machines," HLT-NAACL, pp.233-240, 2004.
4 H. A. Schwartz, F. Gomez, and C. Millward, "A Semantic Feature for Verbal Predicate and Semantic Role Labeling using SVMs," FLAIRS Conference, pp.213-218, 2008.
5 T. Mitsumori, M. Murata, Y. Fukuda, K. Doi, and H. Doi, "Semantic Role Labeling using Support Vector Machines," Association for Computational Linguistics, pp.197-200, 2005.
6 R. T. Tsai, W. Chou, Y. Su, Y. Lin, C. Sung, H. Dai, I. T. Yeh, W. Ku, T. Sung, and W. Hsu, "BIOSMILE: A Semantic Role Labeling System for Biomedical Verbs using a Maximum-Entropy Model with Automatically Generated Template Features," BMC bioinformatics, Vol.8, No.325, pp.1-15, 2007.   DOI
7 N. Kwon, M. Fleischman, and E. Hovy, "Framenet-based Semantic Parsing using Maximum Entropy Models," Proceedings of the 20th International Conference on Computational Linguistics, 2004.
8 T. Liu, W. Che, and S. Li, "Semantic Role Labeling with Maximum Entropy Classifier," Journal of Software, Vol.18, No.3, pp.565-573, 2007.   DOI
9 Z. P. Jiang and H. T. Ng., "Semantic Role Labeling of NomBank: A Maximum Entropy Ap-proach," Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp.138-145, 2006.
10 W. Aziz, M. Rios, and L. Specia, "Improving Chunk-based Semantic Role Labeling with Lexical Features," Proceedings of Recent Advances in Natural Language Processing, pp.226-232, 2011.
11 E. Moreau and I. Tellier, "The Crotal SRL System: A Generic Tool based on Tree Structured CRF," Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task, pp.91-96, 2009.
12 J. Lafferty, A. McCallum, and F. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," Proceedings of the 18th International Conference on Machine Learning, pp.282-289, 2001.
13 F. Sha and F. Pereira, "Shallow Parsing with Conditional Random Fields," Proceedings of the Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics, pp.213-220, 2003.
14 S. Arora, F. Lin, H. Shima, and M. Wang, "Tree Conditional Random Fields for Japanese Semantic Role Labeling," Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA., 2008.
15 M. Palmer, D. Gildea, and P. Kingsbury, "The Proposition Bank: An Annotated Corpus of Seman-tic Roles," Computational Linguistics, Vol.31, No.1, pp.71-106, 2005.   DOI
16 Y. Kim, H. Chae, B. Snyder, and Y. Kim. "Training a Korean SRL System with Rich Morphological Features," Association for Computational Linguistics (ACL). pp.637-642, 2014.
17 P. Resnik, "Using Information Content to Evaluate Semantic Similarity in a Texonomy," Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp.448-453, 1995.
18 T. Cohn and P. Blunsom, "Semantic Role Labelling with Tree Conditional Random Fields," Proceedings of the Ninth Conference on Computational Natural Language Learning, pp.169-172, 2005.
19 M. Seok, C. Park, J. Kim, H. Song, and Y. Kim, "Korean Semantic Role Labeling using Korean PropBank Frame Files," Proceeding of the International Multi-Conference on Engineering and Technology Innovation, 2015.
20 E. Terra and C. L A Clarke, "Frequency Estimates for Statistical Word Similarity Measures," Proceeding of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp.165-172, 2003.
21 T. Joachims, "Learning to Classify Text Using Support Vector Machines: The Springer International Series in Engineering and Computer Science," New York, NY., 2002.
22 K. Crammer and Y. Singer, "On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines," Journal of Machine Learning Research, Vol.2, pp.265-292, 2001.
23 I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, "Support Vector Ma-chine Learning for Interdependent and Structured Output Spaces," Proceedings of the Twenty-first International Conference on Machine Learning, pp.104-111, 2004.
24 C. Sutton and A. McCallum, "An Introduction to conditional random fields," Foundation and Trends in Machine Learning, Vol.4, No.4, pp.267-373, 2006.   DOI