  This paper attempts to analyze the Korean sentence classification system for
a chatbot. Sentence classification is the task of classifying an input sentence
based on predefined categories. However, spelling or space error contained in
the input sentence causes problems in morphological analysis and tokenization.
This paper proposes a novel approach of Integrated Eojeol (Korean syntactic
word separated by space) Embedding to reduce the effect that poorly analyzed
morphemes may make on sentence classification. It also proposes two noise
insertion methods that further improve classification performance. Our
evaluation results indicate that the proposed system classifies erroneous
sentences more accurately than the baseline system by 17%p.0

Computation and Language