A Reranking Model for Korean Morphological Analysis Based on Sequence-to-Sequence Model


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 4, pp. 121-128, Apr. 2018
10.3745/KTSDE.2018.7.4.121,   PDF Download:
Keywords: Sequence-to-Sequence Model, Reranking, Beam-Search, Syllable-Unit Processing
Abstract

A Korean morphological analyzer adopts sequence-to-sequence (seq2seq) model, which can generate an output sequence of different length from an input. In general, a seq2seq based Korean morphological analyzer takes a syllable-unit based sequence as an input, and output a syllable-unit based sequence. Syllable-based morphological analysis has the advantage that unknown words can be easily handled, but has the disadvantages that morpheme-based information is ignored. In this paper, we propose a reranking model as a post-processor of seq2seq model that can improve the accuracy of morphological analysis. The seq2seq based morphological analyzer can generate K results by using a beam-search method. The reranking model exploits morpheme-unit embedding information as well as n-gram of morphemes in order to reorder K results. The experimental results show that the reranking model can improve 1.17% F1 score comparing with the original seq2seq model.


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Cite this article
[IEEE Style]
Y. Choi and K. J. Lee, "A Reranking Model for Korean Morphological Analysis Based on Sequence-to-Sequence Model," KIPS Transactions on Software and Data Engineering, vol. 7, no. 4, pp. 121-128, 2018. DOI: 10.3745/KTSDE.2018.7.4.121.

[ACM Style]
Yong-Seok Choi and Kong Joo Lee. 2018. A Reranking Model for Korean Morphological Analysis Based on Sequence-to-Sequence Model. KIPS Transactions on Software and Data Engineering, 7, 4, (2018), 121-128. DOI: 10.3745/KTSDE.2018.7.4.121.