Presenting Knowledge System of International Trade Terms in Business English Learners＇ Dictionaries

Dictionaries should present business encyclopedic knowledge as well as business linguistic knowledge. This paper, taking Incoterms for international trade as an example （with ＂CIF＂ as the case for analysis）, introduces 7 types of dictionary structures （overall structure, frame stzucture/megastrncture, macrostructure, microstzucture, distribution structure, cross-reference stzucture/mediostrncture and access structure）, summarizes the knowledge system of international trade terms （consisting of ＂international trade＂, ＂sets of rules of international trade terms＂, ＂Incoterms 2010＂, and ＂CIF＂）, and illustrates the methods of systematically presenting the knowledge system of international trade terms through dictionary structures in an English-English-Chinese bilingualized business English learners＇ dictionary with Oxford Business English Dictionary for Learners of English （New Edition） as a reference. Such presentation is intended for enhancing business English learners as dictionary users to grasp the knowledge of international trade terms as a whole.

Knowledge presentation model for QnA web forums

For an extract description of threads information in question and answer （QnA） web forums, it is proposed to construct a QnA knowledge presentation model in the English language, and then an entire solution for the QnA knowledge system is presented, including data gathering, platform building and applications design. With pre-defined dictionary and grammatical analysis, the model draws semantic information, grammatical information and knowledge confidence into IR methods, in the form of statement sets and term sets with semantic links. Theoretical analysis shows that the statement model can provide an exact presentation for QnA knowledge, breaking through any limits from original QnA patterns and being adaptable to various query demands; the semantic links between terms can assist the statement model, in terms of deducing new from existing knowledge. The model makes use of both information retrieval （IR） and natural language processing （NLP） features, strengthening the knowledge presentation ability. Many knowledge-based applications built upon this model can be improved, providing better performance.