Understanding Implicit Entities and Events with Getaruns

Semantic processing represents the new challenge for all applications that require text understanding, as for instance Q/A. In this paper we will highlight the need to couple statistical approaches with deep linguistic processing and will focus on ldquoimplicitrdquo or lexically unexpressed linguistic elements that are nonetheless necessary for a complete semantic interpretation of a text. We will address the following types of ldquoimplicitrdquo entities and events: - grammatical ones, as suggested by a linguistic theories like LFG or similar generative theories; - semantic ones suggested in the FrameNet project, i.e. CNI, DNI, INI; - pragmatic ones: here we will present a theory and an implementation for the recovery of implicit entities and events of (non-) standard implicatures. In particular we will show how the use of commonsense knowledge may fruitfully contribute in finding relevant implied meanings. We will also briefly explore the subject of point of view which is computed by semantic informational structure and contributes the intended entity from whose point of view is expressed a given subjective statement. We also present an evaluation based on section 24 of Penn Treebank as encoded by LFG people in the PARC-700 treebank where lexically unexpressed are adequately classified and diversified.