Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
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In the spoken Chinese language, the time constituents occur frequently, especially in the domain of appointment schedule, ticket booking and hotel reservation etc. However, in the current Chinese-to-English Machine Translation (MT) systems, it is still a problem to deal with the time constituents. According to our test results of some commercial Chinese-to-English MT systems, about 57.1% Chinese time constituents are wrongly translated, and 58.3% of the errors are caused by false recognition and misunderstanding of the time constituents. In the paper, we present a new approach to recognition and understanding of the time constituents in the Chinese language, which is integrated by a shallow level analyzer and a deep level analyzer. The shallow level analysis is realized by a Finite State Transition Network (FSTN). The time constituents are first recognized by the FSTN, and the results are divided into three types. Two types of the results are determinate and one is uncertain. Aimed at the uncertain results, the deep level analyzer checks the semantic context of time constituents, performs necessary phrase structure analysis, and finally decides the type of the time constituents. The approach has been employed in our Chinese-to-English spoken language translation system, which is limited in the domain of hotel reservation. However, the approach is domain-independent. The preliminary experiment has proven that the approach is effective and practical.