In a Machine Translation System (MTS), the number of possible analyses for a given sentence is largely due to the ambiguous characteristics of the source language. In this paper, a mechanism, called "Score Function", is proposed for measuring the "quality" of the ambiguous syntax trees such that the one that best fits interpretation by human is selected. It is featured by incorporating the objectiveness of the probability theory and the subjective expertise of linguists. The underlying uncertainty that is fundamental to linguistic knowledge is also allowed to be incorporated into this system. This feature proposes an easy resolution to select the best syntax tree and provides some strategic advantages for scored parsing. The linguists can al3o be relieved of the necessity to describe the language in strictly "correct" linguistic rules, which, if not impossible, is a very hard task.
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