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Chae, Hee-Rahk. 2015. A Comprehensive Syntactic Analysis of Korean [... V ... V] Expressions I: Auxiliary Predicate Constructions and Case Alternation. Korean Journal of Linguistics, 40-3, 543-578. This paper is an attempt to achieve the goal of providing a comprehensive analysis of Korean [... V1 ... V2] expressions. Under the assumption that these expressions can be divided into auxiliary predicate constructions (APCs), converb/adverbial constructions and idiomatic constructions, we focus on the analysis of APCs in this paper. APCs are distinguished from the others in that the auxiliary predicate V2 takes a VP/S in which V1 occurs as its complement. In typical APCs, the unit containing V1 is a VP, and V1 and V2 share their subject (i.e., their subjects are the same). There are other (non-typical) types of APCs. In some APCs, V2 takes an S (rather than a VP) as its complement and, hence, the two Vs do not share their subject. In other APCs, V2 triggers case alternation in some complements of V1. In addition, different types of V2s (i.e., auxiliary predicates) can occur sequentially and their interactions show interesting phenomena, including complex patterns of case alternation. Despite the large quantity of previous studies, they fail to provide a systematic account of these phenomena. In this paper, under the spirit of mono-stratal approaches, it will be shown that the properties of individual V2s and their interactions can be accounted for effectively with reference to the subcategorization frames of the V2s involved. We do not employ any other mechanisms only for APCs. (Hankuk University of Foreign Studies)

The primary goal of this paper is to find 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 first 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, first building similarity matrices out of the similarity measure values, next drawing dendrograms on the basis of the matricies, then finally 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 final 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.