Quantifier floating is one of the much studied phenomena in natural languages where quantifying expressions may appear in places other than their original prenominal one. Its presence is especially prominent in languages such as Korean that allow more or less free word order. We find that, in addition to what is described in the literature, there are other remarkable regularities in the way the language allows quantifiers to “float” with respect to various constructions including coordination, relative clauses, and embedded clauses. These regularities are captured syntactically in a combinatory categorial grammar (CCG) framework for Korean. We also show how to derive semantic representations for Korean quantifier floating in the same CCG framework.

In this paper, we have proposed Auto-TUS, an automatic transcription module of three union symbols-hyphen, dash and tilde (‘­’, ‘―’, ‘∼’)-using their linguistic contexts. Few previous studies have discussed the problems of ambiguities in transcribing symbols into Korean alphabetic letters. We have classified six different reading formulae of the union symbols, analyzed the left and right contexts of the symbols, and investigated selection rules and distributions between the symbols and their contexts. Based on these linguistic features, 86 stereotyped patterns, 78 rules and 8 heuristics determining the types of reading formulae are suggested for Auto-TUS. This module works modularly in three steps. The pilot test was conducted with three test suites, which contains respectively 418, 987 and 1,014 clusters of words containing a union symbol. Encouraging results of 97.36%, 98.48%, 96.55% accuracy were obtained for three test suites. Our next phases are to develop a guessing routine for unknown contexts of the union symbols by using statistical information; to refine the proper nouns and terminology detecting module; and to apply Auto-TUS on a larger scale.

According to Jeong Ho-seong (1999), Koreans use an average of only 20% of the 508,771 entries of the Korean standard unabridged dictionary. To establish MRD for natural language processing, it is necessary to select Korean lexical units that are used frequently and are considered as basic words. In this study, this selection process is done semi-automatically using the KAIST large corpus. Among about 220,000 morphemes extracted from the corpus of 40,000,000 eojeols, 50,637 morphemes (54,797 senses) are selected. In addition, the coverage of these morphemes in various texts is examined with two sub-corpora of different styles. The total coverage is 91.21 % in formal style and 93.24% in informal style. The coverage of 6,130 first degree morphemes is 73.64% and 81.45%, respectively.

Most of the previous approaches to English agreement phenomena have relied upon only one component of the grammar (e.g., either syntax, or semantics, or pragmatics). This paper argues that interrelationships among different grammatical components play crucial roles in such phenomenon too (cf. Kathol 1999 and Hudson 1999). The paper proposes that, contrary to traditional wisdom, English determiner-noun agreement is morpho-syntactic whereas subject-verb and pronoun-antecedent agreement are reflections of index agreement (cf. Pollard and Sag 1994). The present hybrid analysis of English agreement shows the importance of the interaction of different components of the grammar in accounting for English agreement phenomena. In particular, once we allow morphology to tightly interact with the system of syntax, semantics, or even pragmatics, we could provide a solution to some puzzling English agreement phenomena. This allows a more principled theory of English agreement.

Contrary to the popular view that dative is idiosyncratic to verbs or particular theta roles, the distribution of dative is predictable from the distribution of other cases like nominative, accusative, instrumental, etc. That is, dative behaves like an unmarked or elsewhere choice for Non-Actor/Non-Patient (＝NANP) roles. This paper develops a theory of semantic structural case under the framework of conceptual semantics (Jackendoff 1983, 1990, 1997, 2002). In my proposed analysis, dative is structurally mapped onto NANP entities in the conceptual structure. Important consequences about other peripheral roles are discussed in section 5.

This paper investigates the role of input frequency in the acquisition of verb argument structures based on distributional information of a corpus of utterances derived from the English CHILDES database (MacWhinney 1993). It has been widely accepted that children successfully learn verb argument structures by innate language mechanisms, such as linking rules which connect verb meanings and its syntactic structures. In contrast, an approach to language acquisition called “statistical language learning” has currently claimed that children could succeed in acquiring syntactic structures in the absence of innate language mechanisms, making use of distributional properties of the input. In this paper, I evaluate the feasibility of the statistical learning in acquiring verb argument structures, based on distributional information about locative verbs in parental input. The naturalistic data allow us to investigate to what extent the statistical learning approach can and cannot help children succeed in learning the syntax of locative verbs. Based on the results of English database analysis, I show that there is rich statistical information for learning the syntactic possibilities of locative verbs in parental input, despite some limitations in the statistical learning approach.