  We computed linguistic information at the lexical, syntactic, and semantic
levels for Recognizing Inference in Text (RITE) tasks for both traditional and
simplified Chinese in NTCIR-9 and NTCIR-10. Techniques for syntactic parsing,
named-entity recognition, and near synonym recognition were employed, and
features like counts of common words, statement lengths, negation words, and
antonyms were considered to judge the entailment relationships of two
statements, while we explored both heuristics-based functions and
machine-learning approaches. The reported systems showed robustness by
simultaneously achieving second positions in the binary-classification subtasks
for both simplified and traditional Chinese in NTCIR-10 RITE-2. We conducted
more experiments with the test data of NTCIR-9 RITE, with good results. We also
extended our work to search for better configurations of our classifiers and
investigated contributions of individual features. This extended work showed
interesting results and should encourage further discussion.
