An accurate grammar analyzer that works effectively even with error-ridden sentences input by learners, based on a context-free probabilistic statistical POST (part-of-speech tagged) parser, for a template-automation-based computer-assisted language learning system. For any keyed-in sentence, the parser finds a closest correct sentence to the keyed-in sentence from among the embedded template paths exploiting a highest similarity value, and generates a grammar tree for the correct sentence where some ambiguous words are preassigned by expert language teachers. The system marks the errors under the leaves of the grammar tree by identifying the differences between the keyed-in sentence and the grammar tree of the correct sentence as errors committed by learners. By identifying most frequently recurring grammatical errors of each student, the system sets up a learner's model, providing a unique level of contingent remediation most appropriate to each learner involved.