This paper proposes a new method for recognizing Chinese time expression based on the heuristic error-driven learning.
本文提出了一种基于启发式错误驱动学习的中文时间表达式识别的新方法。
There are two main technologies in time expression recognition:sequence labeling method based on machine learning theory and rule-based method.
时间表达式识别技术主要分为两类：基于机器学习的序列标注方法和基于规则的方法。
Besides temporal nouns, temporal adverbs and words indicating tenses, there are many temporal phrases in the time expression system of the Chinese language.
汉语的时间表达系统中除了有时间名词、时间副词、表时态词语，还存在大量的时间短语。
In this dissertation, it will focus on time expression recognition, which is one of the most important directions within named entity recognition research area.
本文所研究的时间表达式识别，就是命名实体识别领域一项基础而重要的任务。
Combining the data of gas puff Z pinch experiments on Qiangguang I generator, the expression of the line mass is obtained using the implosion time expression which is deduced from snow-plow model.
利用由雪耙模型导出的Z箍缩聚爆时间表达式，并结合“强光一号”Z箍缩实验结果，修正了由一维等熵气体动力学得到的喷嘴气流线质量表达式。
The ending time value must be later than the starting time for the Log Time expression.
日志时间表达式中的结束时间值必须晚于开始时间。
At the same time, expression of PA0011 was found to increase over time in the presence of sub-inhibitory tetracycline and erythromycin concentration.
同时，低于抑制浓度的四环素和红霉素对PA0011基因的表达有明显的增强作用。
At the same time, expression of PA0011 was found to increase over time in the presence of sub-inhibitory tetracycline and erythromycin concentration.
同时，低于抑制浓度的四环素和红霉素对PA0011基因的表达有明显的增强作用。
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