Word segmentation granularity in Korean
This addresses a specific problem in Korean NLP by identifying an improved segmentation approach for parsing tasks, though it appears incremental as it builds on existing granularity proposals.
The paper analyzes different word segmentation granularity levels in Korean language processing and finds that separating only functional morphemes while keeping other suffixes for morphological derivation yields optimal performance for phrase structure parsing, contradicting the previous standard of separating all morphemes.
This paper describes word {segmentation} granularity in Korean language processing. From a word separated by blank space, which is termed an eojeol, to a sequence of morphemes in Korean, there are multiple possible levels of word segmentation granularity in Korean. For specific language processing and corpus annotation tasks, several different granularity levels have been proposed and utilized, because the agglutinative languages including Korean language have a one-to-one mapping between functional morpheme and syntactic category. Thus, we analyze these different granularity levels, presenting the examples of Korean language processing systems for future reference. Interestingly, the granularity by separating only functional morphemes including case markers and verbal endings, and keeping other suffixes for morphological derivation results in the optimal performance for phrase structure parsing. This contradicts previous best practices for Korean language processing, which has been the de facto standard for various applications that require separating all morphemes.