Probabilistic Parsing

A paper in a previous volume [51] explained parsing, which is the process of determining the parses of an input string according to a formal grammar. Also discussed was tabular parsing, which solves the task of parsing in polynomial time by a form of dynamic programming. In passing, we also mentioned that parsing of input strings can be easily generalised to parsing of finite automata.

In applications involving natural language, the set of parses for a given input sentence is typically very large. This is because formal grammars often fail to capture subtle properties of structure, meaning and use of language, and consequently allow many parses that humans would not find plausible.

In natural language systems, parsing is commonly one stage of processing amongst several others. The effectiveness of the stages that follow parsing generally relies on having obtained a small set of preferred parses, ideally only one, from amongst the full set of parses. This is called (syntactic) disambiguation. There are roughly two ways to achieve this. First, some kind of filter may be applied to the full set of parses, to reject all but a few. This filter may look at the meanings of words and phrases, for example, and may be based on linguistic knowledge that is very different in character from the grammar that was used for parsing.