In Korean terminologies, adjectival terms are rare, and the meaning and function associated with adjectives in Indo-European languages are often realized instead in noun form. However, the rarer adjectival terms we, the more they are used in restrictive and repetitive ways in specialized domains. Thus, it is important to distinguish the different senses of these terms. In this work, focusing on semantic modeling in terminology, we distinguish the different senses of adjectival medical terms by applying lexico-semantic criteria (L'Homme, 2004a) and by classifying the semantic category of the arguments of the adjective (Bae and others, 2002). The result not only contributes to enriching medical terminology, but also empirically demonstrates a method for distinguishing the different senses of adjectival medical terms. In this work, we obtained an average of 1.854 senses for each term. We used the KAIST corpus, composed of medical texts (1,500,000 eojeols), and a group of texts on various subjects (40,000,000 eojeols)

Ontology is an indispensable component in intelligent and semantic processing of knowledge and information, such as in semantic web. However, ontology construction requires vast amount of data collection and arduous efforts in processing these un-structured data. This study proposed a methodology to automatically construct and generate ontologies from Sejong Electronic Dictionary. As Sejong Electronic Dictionary is structured in XML format, it can be processed automatically by computer programmed tools into an OWL(Web Ontology Language)-based ontologies as specified in W3C . This paper presents the process and concrete application of this methodology.

Lakoff(1986) claim that sequential fading alone suffices to sanction ATB violations cannot be supported in Korean verbal coordination of TP, since extraction from the conjunction of TP in Korean is impossible regardless of whether the conjuncts are interpreted sequentially or non-sequentially. However, ATB violations are allowed in the coordination of Non-TP, only when the conjuncts are interpreted sequentially. 1 will argue that Non-TP, in coordination are ambiguous between a conjunct and adjunct analysis. Furthermore, a claim made here is that the distinction between the sequential and non-sequential reading in Non-TP coordination is a distinction made by syntax, while the sequential vs. non-sequential reading in TP coordination is derived from semantics or pragmatics.

This paper proposes an informational approach to the dative alternation in English following up on the Stochastic Optimality- Theoretic (OT) model by Bresnan and Nikitina (2003). While Bresnan and Nikitina's stochastic OT model resolves the crucial problem of 'gradience' unavoidably implicated in variation phenomena by applying the notion of probability to linguistic problems, it fails to account for the details of the unusually alternating examples, which normally would not alternate. More importantly, it fails to capture the focus effect involved in the alternation. This paper has worked out all the problematic examples by modifying the Bresnan and Nikitina model. This new account captures not only the unusual behavior of the less-alternating verbs and idioms but also the special focus effect of the common alternating verbs.

Pattern recognition in cognitive agents is based on (i) the uninterpreted input data (e.g. parameter values) provided by the agent's hardware devices and (ii) and interpreted patterns (e.g. templates) provided by the agent's memory. Computationally, the task consists in finding the memory data corresponding best to the input data, for any given input. Once the best fitting memory data have been found, the input is recognized by applying to it the interpretation which happens to be stored with the memorized pattern. This paper presents a fast converging procedure which starts from a few initially recognized items and then analyzes the remainder of the input by systematically checking for items shown by memory to have been related to the initial items in previous encounters. In this way, known patterns are tried first, and only when they have been exhausted, an elementary exploration of the input is commenced. Efficiency is improved further by choosing the candidate to be tested next according to frequency.