Linguistic Knowledge Generator

The difficulties in current NLP applications are seldom due to the lack of appropriate frameworks for encoding our linguistic or extra-linguistic knowledge, hut rather to the fact that we do not know in advance what actual znstances of knowledge should be, even though we know in advance what types of knowledge are required. It normally takes a long time and requires painful trial and error processes to adapt knowledge, for example, in existing MT systems in order to translate documents of a new text-type and of a new subject domain. Semantic classification schemes for words, for example, usually reflect ontologies of subject domains so that we cannot expect a single classification scheme to be effective across different domains. To treat different suhlanguages requires different word classification schemes. We have to construct appropriate schemes for given sublanguages from scratch [1]. It has also been reported that not only knowledge concerned with extra-linguistic domains but also syntactic knowledge, such as subcategorization frames of verbs (which is usually conceived as a par t of general language knowledge), often varies from one sublanguage to another [2]. Though re-usability of linguistic knowledge is currently and intensively prescribed [3], our contention is that the adaptation of existing knowledge requires processes beyond mere re-use. Tha t is,