Coreference Resolution for Korean Using Random Forests


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 11, pp. 535-540, Nov. 2016
10.3745/KTSDE.2016.5.11.535,   PDF Download:
Keywords: Coreference Resolution, Random Forest, Sieve-Guided Features
Abstract

Coreference resolution is to identify mentions in documents and is to group co-referred mentions in the documents. It is an essential step for natural language processing applications such as information extraction, event tracking, and question-answering. Recently, various coreference resolution models based on ML (machine learning) have been proposed, As well-known, these ML-based models need large training data that are manually annotated with coreferred mention tags. Unfortunately, we cannot find usable open data for learning ML-based models in Korean. Therefore, we propose an efficient coreference resolution model that needs less training data than other ML-based models. The proposed model identifies co-referred mentions using random forests based on sieve-guided features. In the experiments with baseball news articles, the proposed model showed a better CoNLL F1-score of 0.6678 than other ML-based models.


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Cite this article
[IEEE Style]
S. Jeong, M. Choi, H. Kim, "Coreference Resolution for Korean Using Random Forests," KIPS Transactions on Software and Data Engineering, vol. 5, no. 11, pp. 535-540, 2016. DOI: 10.3745/KTSDE.2016.5.11.535.

[ACM Style]
Seok-Won Jeong, MaengSik Choi, and HarkSoo Kim. 2016. Coreference Resolution for Korean Using Random Forests. KIPS Transactions on Software and Data Engineering, 5, 11, (2016), 535-540. DOI: 10.3745/KTSDE.2016.5.11.535.