DIAGRAM: a grammar for dialogues
Communications of the ACM
Transition network grammars for natural language analysis
Communications of the ACM
Robust learning, smoothing, and parameter tying on syntactic ambiguity resolution
Computational Linguistics
GPSM: a Generaized Probabilistic Semantic Model for ambiguity resolution
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
A new quantitative quality measure for machine translation systems
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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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 also be relieved of the necessity to describe the language in strictly "correct" linguistic rules, which, if not impossible, is a very hard task.