영어 동사의 의미적 유사도와 논항 선택 사이의 연관성: ICE-GB와 WordNet을 이용한 통계적 검증

Abstract

The primary goal of this paper is to nd a feasible way to answer the question: Does the similarity in meaning between verbs relate to the similarity in their subcategorization?In order to answer this question in a rather concrete way on the basis of a large set of English verbs, this study made use of various language resources, tools,and statistical methodologies. We rst compiled a list of 678 verbs that were selected from the most and second most frequent word lists from the Colins Cobuild English Dictionary, which also appeared in WordNet 3.0. We calculated similarity measures between all the pairs of the words based on the `jcn'algorithm (Jiang and Conrath, 1997) implemented in the WordNet::Similarity module (Pedersen, Patwardhan, and Michelizzi, 2004). The clustering process followed, rst building similarity matrices out of the similarity measure values,next drawing dendrograms on the basis of the matricies, then nally getting 177 meaningful clusters (covering 437 verbs) that passed a certain level set by z-score. The subcategorization frames and their frequency values were taken from the ICE-GB. In order to calculate the Selectional Preference Strength (SPS) of the relationship between a verb and its subcategorizations, we relied on the Kullback-Leibler Divergence model (Resnik, 1996). The SPS values of the verbs in the same cluster were compared with each other, which served to give the statistical values that indicate how much the SPS values overlap between the subcategorization frames of the verbs. Our nal analysis shows that the degree of overlap, or the relationship between semantic similarity and the subcategorization frames of the verbs in English, is equally spread out from the `very strongly related' to the `very weakly related'. Some semantically similar verbs share a lot in terms of their subcategorization frames, and some others indicate an average degree of strength in the relationship, while the others,though still semantically similar, tend to share little in their subcategorization frames.