Collection-based compound noun segmentation for Korean information retrieval

Compound noun segmentation is a key first step in language processing for Korean. Thus far, most approaches require some form of human supervision, such as pre-existing dictionaries, segmented compound nouns, or heuristic rules. As a result, they suffer from the unknown word problem, which can be overcome by unsupervised approaches. However, previous unsupervised methods normally do not consider all possible segmentation candidates, and/or rely on character-based segmentation clues such as bi-grams or all-length n-grams. So, they are prone to falling into a local solution. To overcome the problem, this paper proposes an unsupervised segmentation algorithm that searches the most likely segmentation result from all possible segmentation candidates using a word-based segmentation context. As word-based segmentation clues, a dictionary is automatically generated from a corpus. Experiments using three test collections show that our segmentation algorithm is successfully applied to Korean information retrieval, improving a dictionary-based longest-matching algorithm.