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Issues with the Unergative/Unaccusative Classification of the Intransitive Verbs

机译:不及物动词的不加格/不加格的分类问题

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The paper abandons a strict two-way sub-classification of intransitive verbs into unaccuasative and unergative for Hindi and proposes a distribution plotting of the same in a diffusion chart. The diagnostics tests that Bhatt (2003) applied on Hindi data are ranked for their efficiency of attributing correct sub-class to verbs. The diffusion chart shows that a tripartite classification handles the issue of classification of intransitive verbs in a better manner than the classical binary approach. The tripartite classification is as follows: (1) Verbs that take animate subject and are compatible with adverb of volitionality; (2) Verbs that take animate subject but are not compatible with adverb of volitionality; and (3) Verbs that take inanimate subject. The classification is of immense advantage for various NLP tasks such as machine translation, natural language generation.
机译:本文放弃了对印地语严格的不及物动词的双向分类,将其分为宾语和宾格,并在扩散图中提出了相同的分布图。 Bhatt(2003)对印地语数据进行的诊断测试因其将正确的子类归于动词的效率而排名。扩散图显示,与传统的二元方法相比,三方分类以更好的方式处理了不及物动词的分类问题。三方分类如下:(1)带有动词的动词,与主语副词相容; (2)以动画为主体但与自愿性副词不相容的动词; (3)带有无生命主语的动词。对于各种NLP任务(例如机器翻译,自然语言生成),分类具有极大的优势。

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