NCCU-MIG at NTCIR-10: Using lexical, syntactic, and semantic features for the RITE tasks
Weijie Huang, Chao-Lin Liu · 2013
We computed linguistic information at the lexical, syntactic, and semantic levels for the RITE (Recognizing Inference in TExt) tasks for both traditional and simplified Chinese in NTCIR-10. Techniques for syntactic parsing, named-entity recognition, and near synonym recognition were employed, and features like counts of common words, sentence lengths, negation words, and anto-nyms were considered to judge the logical relationships of two sentences, while we explored both heuristics-based functions and machine-learning approaches. We focused on the BC (binary clas-sification) task at the preparatory stage, but participated in both BC and MC (multiple classes) evaluations. Three settings were submitted for the formal runs for each task. The best performing settings achieved the second best performance in BC tasks, and were listed in the top five performers in MC tasks for both tradi-tional and simplified Chinese. Categories and Subject Descriptors