This paper addresses a customization process of a Korean-English MT system for patent translation. The major customization steps include terminology construction, linguistic study, and the modification of the existing analysis and generation-module. T o our knowledge, this is the first worth-mentioning large-scale customization effort of an MT system for Korean and English. This research was performed under the auspices of the MIC (Ministry of Information and Communication) of Korean government. A prototype patent MT system for electronics domain was installed and is being tested in the Korean Intellectual Property Office.

This paper addresses the workflow for terminology construction for Korean-English patent MT system. The workflow consists of the stage for setting lexical goals and the semi- automatic terminology construction stage. As there is no comparable system, it is difficult to determine how many terms are needed. To estimate the number of the needed terms, we analyzed 45,000 patent documents. Given the limited time and budget, we resorted to the semi-automatic methods to create the bilingual term dictionary in electronics domain. We will show that parenthesis information in Korean patent documents and bilingual title corpus can be successfully used to build a bilingual term dictionary.

This paper describes a sentence pattern-based English-Korean machine translation system backed up by a rule-based module as a solution to the translation of long sentences. A rule-based English-Korean MT system typically suffers from low translation accuracy for long sentences due to poor parsing performance. In the proposed method we only use chunking information on the phrase-level of the parse result (i.e. NP, PP, and AP). By applying a sentence pattern directly to a chunking result, the high performance of analysis and a good quality of translation are expected. The parsing efficiency problem in the traditional RBMT approach is resolved by sentence partitioning, which is generally assumed to have many problems. However, we will show that the sentence partitioning has little side effect, if any, in our approach, because we use only the chunking results for the transfer. The coverage problem of a pattern-based method is overcome by applying sentence pattern matching recursively to the sub-sentences of the input sentence, in case there is no exact matching pattern to the input sentence.

This paper describes our ongoing project “Korean-Chinese Machine Translation System”. The main knowledge of our system is verb patterns. Each verb can have several meanings and each meaning of a verb is represented by a verb pattern. A verb pattern consists of a source language pattern part for the analysis and the corresponding target language pattern part for the generation. Each pattern part, according to the degree of generality, contains lexical or semantic information for the arguments or adjuncts of each verb meaning. In this approach, accurate analysis can directly lead to natural and correct generation. Furthermore as the transfer mainly depends upon verb patterns, the translation rate is expected to go higher, as the size of verb pattern grows larger.

This paper addresses the problems of the so-called ‘Multiple-Subject Constructions’ in Korean-to-English and Korean-to-German MT. They are often encountered in a dialogue, so that they must be especially taken into account in designing a spoken-language translation system. They do not only raise questions about their syntactic and semantic nature but also cause such problems as structural changes in the MT. The proper treatment of these constructions is also of importance in constructing a multilingual MT-System, because they are one of the major characteristics which distinguish the so-called ‘topic-oriented’ languages such as Korean and Japanese from the ‘subject-oriented’ languages such as English and German. In this paper we employ linguistic knowledge such as subcategorization, linear precedence and lexical functions for the analysis and the transfer of the constructions of this sort. Using the proposed methods, the specific transfer-rules for each language pair can be avoided.