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Korean Abstract Meaning Representation (AMR) Guidelines and Corpus for Graph-structured Meaning Representations
Hyonsu Choe, Jiyoon Han, Hyejin Park, Taehwan Oh, Seokwon Park, Hansaem Kim
http://doi.org/10.5626/JOK.2020.47.12.1134
This paper introduces the Korean Abstract Meaning Representation (AMR) Guideline v1.0. AMR is a graph-based meaning representation system and is one of the most significant frameworks for meaning representation. The Korean AMR Guideline is a product of the study that analyzed and localized the AMR Guideline 1.2.6 on the basis of the features of the Korean language has. The Korean AMR corpus can be used for implementation of semantic parser, which is the core of Natural Language Understanding technology, and can be used for NLU/NLG tasks such as Machine Reading Comprehension, Automatic Summarization. The Korean AMR Corpus built depending on this guideline comprises 896 sentences, or 10,414 words (eojeol) for now.
Improving Recall for Context-Sensitive Spelling Correction Rules using Conditional Probability Model with Dynamic Window Sizes
Hyunsoo Choi, Hyukchul Kwon, Aesun Yoon
The types of errors corrected by a Korean spelling and grammar checker can be classified into isolated-term spelling errors and context-sensitive spelling errors (CSSE). CSSEs are difficult to detect and to correct, since they are correct words when examined alone. Thus, they can be corrected only by considering the semantic and syntactic relations to their context. CSSEs, which are frequently made even by expert wiriters, significantly affect the reliability of spelling and grammar checkers. An existing Korean spelling and grammar checker developed by P University (KSGC 4.5) adopts hand-made correction rules for correcting CSSEs. The KSGC 4.5 is designed to obtain very high precision, which results in an extremely low recall. Our overall goal of previous works was to improve the recall without considerably lowering the precision, by generalizing CSSE correction rules that mainly depend on linguistic knowledge. A variety of rule-based methods has been proposed in previous works, and the best performance showed 95.19% of average precision and 37.56% of recall. This study thus proposes a statistics based method using a conditional probability model with dynamic window sizes. in order to further improve the recall. The proposed method obtained 97.23% of average precision and 50.50% of recall.
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