Abstract

A bootstrap approach for extracting unknown words from a Chinese text corpus is proposed. Instead of using a non-iterative segmentation-detection approach, the proposed method iteratively extracts the new words and adds them into the lexicon. Then the augmented dictionary, which includes potential unknown words (in addition to known words), is used in the next iteration to re-segment the input corpus until stop conditions are reached. Experiments show that both the precision and recall rates of segmentation are improved.

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