Single-Classifier Memory-Based Phrase Chunking
Single-Classifier Memory-Based Phrase Chunking
复制标题
基于单分类器记忆的短语分块
DOI：
10.3115/1117601.1117640
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Antal van den Bosch
中科院分区：
文献类型：
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作者：
J. Veenstra;Antal van den Bosch
在CoNLL-2000的共享任务中，单词和标签构成了预测丰富短语切分代码的基本多值特征。虽然包含《华尔街日报》词性标签的标签特征(Marcus等人，1993)大约有45个值，但单词特征有10,000多个值。在我们的研究中，我们考察了在TimBL软件系统(Daelemans等人，2000年)中实现的基于记忆的学习如何处理这些特征。我们将搜索限制在单个分类器上，从而明确忽略了构建有望提高准确率的元学习分类器体系结构的可能性。考虑到这一限制，我们探索了以下内容：1.缺省设置(多值特征、重叠度量、特征加权)下的TimBL的泛化精度。MVDM的使用(Stanill和Waltz，1986；Cost和Salzberg，1993)(第2节)，它在中高频词值对上应该起到很好的作用，但在低频词值对上可能不起作用。将特征值直接解包为二进制特征。在一些任务上，我们发现将多值特征分解成多个二值特征可以提高分类器的性能。基于所有未打包的特征值对复杂特征进行启发式搜索，并将这些复杂特征用于分类任务。
In the shared task for CoNLL-2000, words and tags form the basic multi-valued features for predicting a rich phrase segmentation code. While the tag features, containing WSJ part-of-speech tags (Marcus et al., 1993), have about 45 values, the word features have more than 10,000 values. In our study we have looked at how memory-based learning, as implemented in the TiMBL software system (Daelemans et al., 2000), can handle such features. We have limited our search to single classifiers, thereby explicitly ignoring the possibility to build a meta-learning classifier architecture that could be expected to improve accuracy. Given this restriction we have explored the following:1. The generalization accuracy of TiMBL with default settings (multi-valued features, overlap metric, feature weighting).2. The usage of MVDM (Stanfill and Waltz, 1986; Cost and Salzberg, 1993) (Section 2), which should work well on word value pairs with a medium or high frequency, but may work badly on word value pairs with low frequency.3. The straightforward unpacking of feature values into binary features. On some tasks we have found that splitting multi-valued features into several binary features can enhance performance of the classifier.4. A heuristic search for complex features on the basis of all unpacked feature values, and using these complex features for the classification task.