  We report our development of a simple but fast and efficient inductive
unsupervised semantic tagger for Chinese words. A POS hand-tagged corpus of
348,000 words is used. The corpus is being tagged in two steps. First, possible
semantic tags are selected from a semantic dictionary(Tong Yi Ci Ci Lin), the
POS and the conditional probability of semantic from POS, i.e., P(S|P). The
final semantic tag is then assigned by considering the semantic tags before and
after the current word and the semantic-word conditional probability P(S|W)
derived from the first step. Semantic bigram probabilities P(S|S) are used in
the second step. Final manual checking shows that this simple but efficient
algorithm has a hit rate of 91%. The tagger tags 142 words per second, using a
120 MHz Pentium running FOXPRO. It runs about 2.3 times faster than a Viterbi
tagger.
