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Identifying Participation of Individual Verbs or VerbNet Classes in the Causative Alternation

机译:识别单个动词或VerbNet类在因果交替中的参与

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Verbs that participate in diathesis alternations have different semantics in their different syntactic environments, which need to be distinguished in order to process these verbs and their contexts correctly. We design and implement 8 approaches to the automatic identification of the causative alternation in English (3 based on VerbNet classes, 5 based on individual verbs). For verbs in this alternation, the semantic roles that contribute to the meaning of the verb can be associated with different syntactic slots. Our most successful approaches use distributional vectors and achieve an F1 score of up to 79% on a balanced test set. We also apply our approaches to the distinction between the causative alternation and the unexpressed object alternation. Our best system for this is based on syntactic information, with an F1 score of 75% on a balanced test set.
机译:参与质素替换的动词在其不同的句法环境中具有不同的语义,需要对其进行区分,以便正确处理这些动词及其上下文。我们设计并实现了8种方法来自动识别英语中的因果交替(3种基于VerbNet类,5种基于单个动词)。对于这种交替形式的动词,有助于动词含义的语义角色可以与不同的句法位置关联。我们最成功的方法是使用分布向量,并在平衡的测试集上获得高达79%的F1分数。我们还将我们的方法用于使原因交替与未表达的宾语交替之间的区别。为此,我们最好的系统是基于语法信息的,在均衡的测试集中,F1分数达到75%。

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