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Sensicon: An Automatically Constructed Sensorial Lexicon

机译:Sensicon:自动构建的感官词汇

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摘要

Connecting words with senses, namely, sight, hearing, taste, smell and touch, to comprehend the sensorial information in language is a straightforward task for humans by using commonsense knowledge. With this in mind, a lexicon associating words with senses would be crucial for the computational tasks aiming at interpretation of language. However, to the best of our knowledge, there is no systematic attempt in the literature to build such a resource. In this paper, we present a sensorial lexicon that associates English words with senses. To obtain this resource, we apply a computational method based on bootstrapping and corpus statistics. The quality of the resulting lexicon is evaluated with a gold standard created via crowd-sourcing. The results show that a simple classifier relying on the lexicon outperforms two baselines on a sensory classification task, both at word and sentence level, and confirm the soundness of the proposed approach for the construction of the lexicon and the usefulness of the resource for computational applications.
机译:通过使用常识,将单词与视觉,听觉,味觉,气味和触觉等感官联系起来,以理解语言中的感官信息,对人类来说是一项直接的任务。考虑到这一点,将单词与感觉相关联的词典对于旨在解释语言的计算任务至关重要。但是,据我们所知,文献中没有系统地尝试建立这种资源。在本文中,我们提出了将英语单词与感官联系起来的感官词典。为了获得此资源,我们应用了一种基于自举和语料统计的计算方法。使用通过众包创建的金标准来评估生成的词典的质量。结果表明,在词汇和句子层面上,依赖于词典的简单分类器在感觉分类任务上的表现优于两个基线,并且证实了所提出的词典构建方法的合理性以及该资源对计算应用的实用性。

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