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Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora

机译:从浅析语料库中无监督获取动词子分类框架

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摘要

In this paper, we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames (SCFs) from general and domain corpora. The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle (MDL). First experiments in this direction were carried out on Italian verbs with encouraging results.
机译:在本文中,我们报告了从普通语料库和领域语料库无监督地自动获取意大利语和英语动词子分类框架(SCF)的实验。所提出的技术基于有限数量的搜索试探法,在语法上浅析浅析的语料库上运行,该试探法不依赖于任何先前关于SCF的词汇语法学知识。尽管是初步的,但报告的结果与最新的词汇习得系统一致。使用最小描述长度原则(MDL),通过对共享具有相同分布框架的动词进行聚类,也探讨了共享相似SCF分布的动词是否碰巧也共享相似的语义属性的问题。在这个方向上的第一个实验是用意大利语动词进行的,结果令人鼓舞。

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