Abstract
This paper explores the feasibility of modelling concept concreteness perceived by humans and representing it in computational semantic lexicons, addressing an issue at the crossroads of computational linguistics, lexicography, and psycholinguistics. The inherent distinction between concrete words and abstract words in psychology has relied mostly on subjective human ratings. This practice is hardly scalable and does not consider the effect of polysemy. In view of this, we attempt to obtain a measure of concreteness from dictionary definitions comparable to human judgement, capitalising on conventional lexicographic assumptions and the regularities exhibited in the surface structures of sense definitions. The structural pattern of a definition is analysed and scored on a 7-point scale of concreteness ratings. The definition scores turned out to be quite effective for a dichotomous distinction between concrete and abstract concepts and more consistent with human ratings for the former. Beyond the two-way distinction, however, the results were more variable. The study has thus revealed the potentials and limitations of our approach, suggesting that different defining styles probably reflect the describability of concepts, and describability alone may not be sufficient for differentiating the degree of concreteness. The range of definition patterns has to be reconsidered, in combination with other inseparable factors constituting our perception of concreteness, for better modelling on a finer scale of concreteness distinction to enrich semantic lexicons for natural language processing.
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Notes
The three resources are sampled for the different and typical defining principles they represent. Other examples like the Oxford Advanced Learner’s Dictionary are equally usable.
The conventional defining styles outlined in this section have assumed that words can be defined in isolation. This is particularly challenged in the COBUILD series of dictionaries, which highlights their full-sentence definitions embedding the typical syntactico-collocational context. See Hanks (1987) for a detailed account, and Rundell (2006) for its strengths and weaknesses.
Other forms of noun definitions include if–then statements such as ‘If you say that something has X, you mean Y’, and those starting with ‘you’ explicitly telling how one uses the word, such as ‘You can use X in expressions such as …’. These forms are simply taken as Category 1 in Table 1 in the current study.
The WordNet and LDOCE definitions were first made complete sentences in the form of ‘X is Y’ by adding the headword as subject such as ‘car is a motor vehicle …’ before feeding to the parser. The COBUILD definitions are already complete sentences, so they were directly parsed.
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Acknowledgments
The work described in this paper was partially supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 1508/06H), and the Department of Chinese, Translation and Linguistics of the City University of Hong Kong. The author would like to thank the anonymous reviewers for their valuable comments and suggestions.
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Kwong, O.Y. The potentials and limitations of modelling concept concreteness in computational semantic lexicons with dictionary definitions. Lang Resources & Evaluation 47, 1149–1161 (2013). https://doi.org/10.1007/s10579-013-9228-1
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DOI: https://doi.org/10.1007/s10579-013-9228-1