Mining Uncertain Sentences with Multiple Instance Learning
Distinguishing uncertain information from factual ones in online texts is of essential importance in information extraction, because uncertain information would mislead systems to find useless even fault information. In this paper, we propose a method for detecting uncertain sentences with multiple instance learning (MIL). Based on the basic assumption, we derive two new constraints for estimating the weight vector by defining a probability margin, which is used in an online learning algorithm known as Passive-Aggressive algorithm. To demonstrate the effectiveness of our method, we experiment on the biomedical corpus. Compared with an intuitive method with conventional single instance learning (SIL), our method provide higher performance by raising the performance from 79.07％ up to 82.55％, over 3％ improvement.
Uncertain sentence Multiple instance learning Passive-Aggressive algorithm
Feng Ji Xipeng Qiu Xuanjing Huang
School of Computer Science and Technology,Fudan University Shanghai 201203 China
国际会议
6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)
重庆
英文
521-528
2010-11-19（万方平台首次上网日期，不代表论文的发表时间）