  A patent is a property right for an invention granted by the government to
the inventor. An invention is a solution to a specific technological problem.
So patents often have a high concentration of scientific and technical terms
that are rare in everyday language. The Chinese word segmentation model trained
on currently available everyday language data sets performs poorly because it
cannot effectively recognize these scientific and technical terms. In this
paper we describe a pragmatic approach to Chinese word segmentation on patents
where we train a character-based semi-supervised sequence labeling model by
extracting features from a manually segmented corpus of 142 patents, enhanced
with information extracted from the Chinese TreeBank. Experiments show that the
accuracy of our model reached 95.08% (F1 score) on a held-out test set and
96.59% on development set, compared with an F1 score of 91.48% on development
set if the model is trained on the Chinese TreeBank. We also experimented with
some existing domain adaptation techniques, the results show that the amount of
target domain data and the selected features impact the performance of the
domain adaptation techniques.
