Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features
Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features
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发表时间：
2014-05
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通讯作者：
Daisuke Kawahara;Martha Palmer
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作者：
Daisuke Kawahara;Martha Palmer
提出了一种基于VerbNet的有监督的动词词义消歧方法。大多数以前的监督方法动词意义消歧创建一个分类器，每个动词达到一个频率阈值。然而，这些方法有一个重要的实际问题，即它们不能应用于罕见或看不见的动词。为了克服这个问题，我们创建了一个单一的分类器，应用于罕见的或看不见的动词在一个新的文本。这个单一的分类器还利用了动词及其修饰语的广义语义特征，以便更好地处理罕见或看不见的动词。我们的实验结果表明，所提出的方法实现了等效的性能，每动词分类器，这不能适用于看不见的动词。我们的分类器可以用来提高分类的词汇资源的动词，如VerbNet，在半自动的方式，并可能扩大这些资源的覆盖范围，以新的动词。
We present a supervised method for verb sense disambiguation based on VerbNet. Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold. These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs. In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text. This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs. Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs. Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs.