Computer Science – Computation and Language
Scientific paper
2007-11-21
Dans Proceedings of the Language Resource and Evaluation Consference (LREC) - Morphological annotation of Korean with Directly
Computer Science
Computation and Language
Scientific paper
This article describes an exclusively resource-based method of morphological annotation of written Korean text. Korean is an agglutinative language. Our annotator is designed to process text before the operation of a syntactic parser. In its present state, it annotates one-stem words only. The output is a graph of morphemes annotated with accurate linguistic information. The granularity of the tagset is 3 to 5 times higher than usual tagsets. A comparison with a reference annotated corpus showed that it achieves 89% recall without any corpus training. The language resources used by the system are lexicons of stems, transducers of suffixes and transducers of generation of allomorphs. All can be easily updated, which allows users to control the evolution of the performances of the system. It has been claimed that morphological annotation of Korean text could only be performed by a morphological analysis module accessing a lexicon of morphemes. We show that it can also be performed directly with a lexicon of words and without applying morphological rules at annotation time, which speeds up annotation to 1,210 word/s. The lexicon of words is obtained from the maintainable language resources through a fully automated compilation process.
Berlocher Ivan
Huh Hyun-Gue
Laporte Eric
Nam Jee-Sun
No associations
LandOfFree
Morphological annotation of Korean with Directly Maintainable Resources does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Morphological annotation of Korean with Directly Maintainable Resources, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Morphological annotation of Korean with Directly Maintainable Resources will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-414719