Adapting stochastic LFG input for semantics

Abstract

LFG c(onstituent)-structure and f(unctional)-structure analyses provide the detailed syntactic structures necessary for subsequent semantic analy- sis. The f-structure encodes grammatical functions as well as semantically relevant features like tense and number. The c-structure, in conjunction with the -mapping, provides the information on linear precedence necessary for semantic scope and anaphora resolution. In this paper, we present a system in which a stochastic LFG-like grammar of English provides the input to the se- mantic processing. The LFG-like grammar uses stochastic methods to create a c-structure and a proto f-structure. A set of ordered rewrite rules augments and reconfigures the proto f-structure to add more information to the stochas- tic output, thereby creating true LFG f-structures with all of the features that the semantics requires. Evaluation of the resulting derived f-structures and of the semantic representations based on them indicates that the stochastic LFG- like grammar can be used to produce input to the semantics. These grammars combine the advantages of LFG structures, e.g. the explicit encoding of gram- matical functions, with the advantages of stochastic systems, e.g. providing connected parses in the face of less-than-ideal input.