This project started several years ago on a set of questions related to the DLT systems for question/answer. The set of questions to be addressed included some recent work on the problem of questions and answers in natural language. The approach is broadly an evolutionary process whereby a set of input texts and an internal representation was developed into a working DLT system which then learned some common syntactic structures. This way the systems can learn not only to talk and answer together, but eventually they can also learn how to write together, at least the basic form of writing. In particular, the systems learn about how to compose answers. The working system is able to automatically compose answers to questions posed in different ways by asking question-answering questions in other formats in multiple languages and languages not necessarily related to the query language they are asked in, for example, Chinese, Russian and English question and answer systems. This project involved a number of interrelated projects. In our main contribution we developed and used an NLPE-based DLT system for translating Russian into English, which is one of the most common and well known natural language question systems. The system is able to answer the question “What day of the week do Russian teachers teach their students in winter?” in the standard form (all the Russian grammar rules for answering such questions for Russian are the same, the word order rules are the same in any case, the grammar of the sentences depends on the sentence tense and word order of the question). Moreover, we found that the question and answer system can translate into English questions about the weather (when is it warm) and about the time of day in Russian (when in Moscow is it hot?), also about the situation of people in Moscow at any time of the year. A similar question-answering system was developed and used for Chinese, which could be used to translate a question in English to Chinese and a question in English to English using a version of the NAPE, while the Russian system was also able to translate a question in the same way.
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Proceedings of NTCIR-5 Workshop Meeting, December 6-9, 2005, Tokyo, Japan Chinese Question Answering using the DLT System at NT CIR 2005 Richard F. E. Ratcliffe* Natural Language Engineering and Web
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