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A Deep Learning-based Two-Steps Pipeline Model for Korean Morphological Analysis and Part-of-Speech Tagging
http://doi.org/10.5626/JOK.2021.48.4.444
Recent studies on Korean morphological analysis using artificial neural networks have usually performed morpheme segmentation and part-of-speech tagging as the first step with the restoration of the original form of morphemes by using a dictionary as the postprocessing step. In this study, we have divided the morphological analysis into two steps: the original form of a morpheme is restored first by using the sequence-to-sequence model, and then morpheme segmentation and part-of-speech tagging are performed by using BERT. Pipelining these two steps showed comparable performance to other approaches, even without using a morpheme restoring dictionary that requires rules or compound tag processing.
Morpheme-based Korean Word Vector Generation Considering the Subword and Part-Of-Speech Information
http://doi.org/10.5626/JOK.2020.47.4.395
Word vectors enable finding the relationship between words by vector computation. They are also widely used as pre-trained data for high-level neural network programs. Various modified models from English models have been proposed for the generation of Korean word vectors, with various segmentation units such as Eojeol(word phrase), morpheme, syllable and Jaso. In this study, we propose Korean word vector generation methods that segment Eojeol into morphemes and convert them into subwords comprising either syllable or Jaso. We also propose methods using Part-Of-Speech tags provided in the pre-processing to reflect semantic and syntactic information regarding the morphemes. Intrinsic and extrinsic experiments showed that the method using morpheme segments with Jaso subwords and additional Part-Of-Speech tags showed better performance than others under the condition that the target data are normal text and not as grammatically incorrect.
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