Exploring Lexical, Syntactic, and Semantic Features for Chinese Textual Entailment in NTCIR RITE Evaluation Tasks

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.Comment: 20 pages, 1 figure, 26 tables, Journal article in Soft Computing (Spinger). Soft Computing, online. Springer, Germany, 201

The WHUTE System in NTCIR-9 RITE Task

This paper describes our system of recognizing textual entailment for RITE Chinese subtask at NTCIR-9. We build a textual entailment recognition framework and implement a system that employs string, syntactic, semantic and some specific features for the recognition. To improve the system’s performance, a twostage recognition strategy is utilized, which first judge entailment or no entailment, and then contradiction or independence of the pairs in turn. Official results show that our system achieves a 73.71 % performance in BC subtask, 60.93 % in MC subtask and 48.76 % in RITE4QA subtask