INCREMENTAL PARSING WITH LD/TLP‐TAGS
Peter Poller · Computational Intelligence · 1994
A main purpose of natural language systems (NL‐systems) is the simulation of human speech behavior, which divides into generation and analysis, in all kinds of situations. Humans process natural language in an incremental style, which means that sentences are generated or analyzed in a piecemeal fashion. Observations show that there are bidirectional dependencies between these processes. During parsing, hypotheses for the continuation of an utterance may be generated. During generation, the output may be parsed in order to detect ambiguities. This paper presents a two‐level incremental parsing algorithm for LD/TLP‐TAGs on the basis of Earley's context‐free parser. LD/TLP‐TAG is a new extension for TAG that increases the descriptive power of the formalism without changing its generative power. Elementary rules are split into local dominance description (LD) and linear precedence rules (TLP) allowing for generalizations of ordering constraints. The presented algorithm is used within an anticipation feedback loop for the incremental syntactic generator TAG‐GEN.