Abstract
Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely by previous studies. However, the model has a problem that it cannot utilize interactions among the tasks. This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-to-sequence multi-task model for training the two tasks simultaneously without any explicit regularization. The experiments prove the proposed model achieves the state-of-the-art performance.
Highlights
Korean is an agglutinative language (Song, 2006)
This paper proposes a model to train morpheme processing and POS tagging simultaneously in Korean morphological analysis
Korean morphological analysis is different from existing NLP tasks such as English POS tagging (Toutanova et al, 2003; Manning, 2011), and joint word segmentation and POS tagging for Chinese (Zhang and Clark, 2008; Shao et al, 2017; Chen et al, 2017)
Summary
Korean is an agglutinative language (Song, 2006). it is a fundamental step for understanding a sentence to analyze the grammatical structure of eojeols, where an eojeol is a linguistic unit segmented by a white space. The goal of Korean morphological analyzer is to decompose and recover morphemes from eojeols precisely (morpheme processing), and to assign POS tags to the decomposed and/or recovered morphemes accurately according to a context (POS tagging). Decompose and recover morphemes from eojeols or assign so-called POSMORPH tags (Heigold et al, 2016), and an actual POS tag sequence is determined or resolved from the POSMORPH tags using a sequential labeling algorithm. This pipeline model suffers from two kinds of weaknesses. This paper proposes a model to train morpheme processing and POS tagging simultaneously in Korean morphological analysis. P(m|x) corresponds to morpheme processing, while p(t|m, x) is POS tagging
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