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An efficient dictionary access method for morphological analysis

Authors: Kazuaki Ando; Takako Tsuji; Masao Fuketa; Jun-ichi Aoe;

An efficient dictionary access method for morphological analysis

Abstract

Proposes an efficient dictionary access method for morphological analysis of oriental languages by extending Aho and Corasick's (1990) pattern matching machine. The proposed method is a simple and efficient algorithm to find all possible substrings in an input sentence and during a single pass. It stores the relations of grammatical connectivity of adjacent words into the output functions. Therefore, the costs of checking connections between the adjacent words can be reduced by using the connectivity relations. Furthermore, the method of constructing the grammatical connectivity relations is described. Finally, the proposed method is verified by theoretical analysis and an experimental estimation is supported by a computer simulation with a 100,000-word dictionary. From the simulation results, it turns out that the proposed method is 49.9% faster (in CPU time) than the traditional trie approach. In addition, the number of candidates for checking connections was 25.5% less than that of the original morphological analysis.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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