This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge. The identification phase combines the use of conditional random fields along with a post-processing identification pipeline, whereas the normalization phase is carried out using NorMA, an open-source rule-based temporal normalizer. We investigate the performance variation with respect to different feature types. Specifically, we show that the use of WordNet-based features in the identification task negatively affects the overall performance, and that there is no statistically significant difference in using gazetteers, shallow parsing and propositional noun phrases labels on top of the morphological features. On the test data, the best run achieved 0.95 (P), 0.85 (R) and 0.90 (F1) in the identification phase. Normalization accuracies are 0.84 (type attribute) and 0.77 (value attribute). Surprisingly, the use of the silver data (alone or in addition to the gold annotated ones) does not improve the performance.

本文介绍了一个用于TempEval-3挑战的时间表达式识别和归一化系统ManTIME，其中识别阶段结合了条件随机场和后处理识别管道，而规范化阶段则使用NorMA，探讨了不同特征类型对性能的影响。最好的运行在标识阶段达到0.95（P），0.85（R）和0.90（F1），规一化精度分别为0.84（类型属性）和0.77（值属性）

ManTIME: TempEval-3 挑战赛中的时间表达式识别和归一化