A New Perceptron Algorithm for Sequence Labeling with Non-Local Features
A New Perceptron Algorithm for Sequence Labeling with Non-Local Features
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DOI：
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发表时间：
2007-06
期刊：
影响因子：
4.4
通讯作者：
Jun'ichi Kazama;Kentaro Torisawa
中科院分区：
文献类型：
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作者：
Jun'ichi Kazama;Kentaro Torisawa
由于考虑到复杂性，我们不能将非局部特征与当前主要的序列标记方法（如CRFs）一起使用。我们提出了一种新的感知器算法，可以使用非局部特征。我们的算法允许使用所有类型的非局部特征，其值由序列和标签确定。在训练过程中同时学习局部特征和非局部特征的权值，保证收敛性。我们给出了来自CoNLL 2003命名实体识别（NER）任务的实验结果，以证明所提出算法的性能。
We cannot use non-local features with current major methods of sequence labeling such as CRFs due to concerns about complexity. We propose a new perceptron algorithm that can use non-local features. Our algorithm allows the use of all types of non-local features whose values are determined from the sequence and the labels. The weights of local and non-local features are learned together in the training process with guaranteed convergence. We present experimental results from the CoNLL 2003 named entity recognition (NER) task to demonstrate the performance of the proposed algorithm.