  Automatic scoring system is extremely complex for any language. Because
natural language itself is a complex model. When we evaluate articles generated
by natural language, we need to view the articles from many dimensions such as
word features, grammatical features, semantic features, text structure and so
on. Even human beings sometimes can't accurately grade a composition because
different people have different opinions about the same article. But a
composition scoring system can greatly assist language learners. It can make
language leaner improve themselves in the process of output something. Though
it is still difficult for machines to directly evaluate a composition at the
semantic and pragmatic levels, especially for Japanese, Chinese and other
language in high context cultures, we can make machine evaluate a passage in
word and grammar levels, which can as an assistance of composition rater or
language learner. Especially for foreign language learners, lexical and
syntactic content are usually what they are more concerned about. In our
experiments, we did the follows works: 1) We use word segmentation tools and
dictionaries to achieve word segmentation of an article, and extract word
features, as well as generate a words' complexity feature of an article. And
Bow technique are used to extract the theme features. 2) We designed a
Turing-complete automata model and create 300+ automatons for the grammars that
appear in the JLPT examination. And extract grammars features by using these
automatons. 3) We propose a statistical approach for scoring a specify theme of
composition, the final score will depend on all the writings that submitted to
the system. 4) We design an grammar hint function for language leaner, so that
they can know currently what grammars they can use.
