A Trainable Rule-based Algorithm for Word Segmentation

This paper presents a trainable rule-based algorithm for performing word segmentation. The algorithm provides a simple, language-independent alternative to large-scale lexicai-based segmenters requiring large amounts of knowledge engineering. As a stand-alone segmenter, we show our algorithm to produce high performance Chinese segmentation. In addition, we show the transformation-based algorithm to be effective in improving the output of several existing word segmentation algorithms in three different languages.

• Type: Conference
• Year: 1997
• Where: ACL
• Authors: David D. Palmer
• Keywords: ACL 1997, ACL 2007, Algorithms, Trainable Rule-based Algorithm, Word Segmentation