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Semantic parsing, i.e., the automatic derivation of meaning representationsuch as an instantiated predicate-argument structure for a sentence, plays acritical role in deep processing of natural language. Unlike all other topsystems of semantic dependency parsing that have to rely on a pipelineframework to chain up a series of submodels each specialized for a specificsubtask, the one presented in this article integrates everything into onemodel, in hopes of achieving desirable integrity and practicality for realapplications while maintaining a competitive performance. This integrativeapproach tackles semantic parsing as a word pair classification problem using amaximum entropy classifier. We leverage adaptive pruning of argument candidatesand large-scale feature selection engineering to allow the largest featurespace ever in use so far in this field, it achieves a state-of-the-artperformance on the evaluation data set for CoNLL-2008 shared task, on top ofall but one top pipeline system, confirming its feasibility and effectiveness.

Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection