Korean compound nouns may be written as a sequence of characters without blanks between unit nouns. For Korean processing systems, Korean compound nouns have to be first segmented into a sequence of unit nouns. However, the segmentation task is difficult because a sequence of characters may be ambiguously segmented to several sequences of appropriate unit nouns. Moreover, this task is not trivial because Korean compound nouns may include many unknown unit nouns.This paper proposes a new method for KCNS (Korean Compound Noun Segmentation) and reports on the application of such a segmentation technique to enhance the performance of an information retrieval system. According to our method, compound nouns are first segmented by using a dictionary and structure patterns. If they are ambiguously segmented, we resolve the ambiguities by using statistical information and a preference rule. Moreover, we employ three kinds of heuristics in order to segment compound nouns with unknown unit nouns.To evaluate KCNS, we use three kinds of data from various domains. Experimental results show that the precision of KCNS's output is approximately 96% on average, regardless of domains. The effectiveness of using the segmented unit nouns provided by KCNS for indexing is proved by improving retrieval performance of our information retrieval system.

In this paper, we propose a feature-based Korean grammar utilizing the learned constraint rules in order to improve parsing efficiency. The proposed grammar consists of feature structures, feature operations, and constraint rules; and it has the following characteristics. First, a feature structure includes several features to express useful linguistic information for Korean parsing. Second, a feature operation generating a new feature structure is restricted to the binary-branching form which can deal with Korean properties such as variable word order and constituent ellipsis. Third, constraint rules improve efficiency by preventing feature operations from generating spurious feature structures. Moreover, these rules are learned from a Korean treebank by a decision tree learning algorithm. The experimental results show that the feature-based Korean grammar can reduce the number of candidates by a third of candidates at most and it runs 1.5 ∼ 2 times faster than a CFG on a statistical parser.

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