Storing Logical Form in a Shared-Packed Forest

There are several types of ambiguity in natural languages, including lexical ambiguity, syntactic (or structural) ambiguity, quantifier scope ambiguity, and anaphora (or ambiguity of reference). Each type of ambiguity must be resolved for a natural language understanding program to be effective. Since syntax often limits the possible meanings of a sentence (and the words in the sentence), natural language processing programs often analyze the structure of a sentence before attempting to determine its meaning. However, additional knowledge sources must often be used in understanding, including selectional restrictions, world knowledge, and contextual information. The use of world knowledge and contextual information often requires inference and hence access to the representations of the sentence and possibly its components. But at the same time, because of ambiguity, a program might not be able to enumerate all of the possible representations for a sentence and its components, since just listing all possible structural analyses for syntactically ambiguous sentences can be impractical, and each structural analysis of a sentence typically produces at least one additional meaning. In this paper, we will focus on the problem of efficiently maintaining syntactic ambiguity while determining the logical representation for a sentence. In particular, we describe an approach that combines shared-packed parse forests with semantic construction routines. This approach allows a program attempting to eliminate ambiguity from a sentence to apply higher level knowledge sources to the logical representations of desired constituents in the parse forest (e.g., it could eliminate alternative parses for a noun phrase (NP) whose representation does not match objects in a world model).