A Discriminative Learning Model for Coordinate Conjunctions
A Discriminative Learning Model for Coordinate Conjunctions
复制标题
并列连词的判别学习模型
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Kazuo Hara
中科院分区：
文献类型：
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作者：
M. Shimbo;Kazuo Hara
提出了一种基于序列比对的并列连词检测与消歧方法。在该方法中，使用平均感知器学习来使替换矩阵适应于来自目标语言和领域的训练数据。为了降低训练数据构建的代价，我们的方法接受缺少完整的逐字对齐标签的训练实例，而只标记并列连词的边界。我们报告了在Genia语料库中检测和消除并列名词短语的良好实验结果，尽管使用了相对较少的训练样本和最少的特征。
We propose a sequence-alignment based method for detecting and disambiguatingcoordinate conjunctions. In this method, averaged perceptron learning is used to adapt the substitution matrix to the training data drawn from the target language and domain. To reduce the cost of training data construction, our method accepts training examples in which complete word-by-word alignment labels are missing, but instead only the boundaries of coordinated conjuncts are marked. We report promising empirical results in detecting and disambiguating coordinated noun phrases in the GENIA corpus, despite a relatively small number of training examples and minimal features are employed.