Jung-Hun Lee  Minho Kim  Hyuk-Chul Kwon

10.5626/JOK.2020.47.2.147

context-sensitive correction contextual embedding Word Embedding Natural Language Processing

This paper focuses on the use of embedding techniques to solve problems in context-sensitive spelling correction and compare the performance of each technique. A vector of words obtained through embedding learning is used to correct the distance between the correction target word and the surrounding context word. In this paper, we tried to improve the correction performance by reflecting the processing of words not included in the learning corpus and surrounding contextual information of the correction words. The embedding techniques used for proofing were divided into word-based embeddings and embeddings that reflected contextual information. This paper performed correction experiments using the embedding techniques, focusing on the above two improvement goals, and obtained reliable correction performance.