Paper: Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence Modeling

ACL ID S13-1043
Title Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence Modeling
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

Implicit arguments are a discourse-level phe- nomenon that has not been extensively stud- ied in semantic processing. One reason for this lies in the scarce amount of annotated data sets available. We argue that more data of this kind would be helpful to improve exist- ing approaches to linking implicit arguments in discourse and to enable more in-depth stud- ies of the phenomenon itself. In this paper, we present a range of studies that empirically val- idate this claim. Our contributions are three- fold: we present a heuristic approach to auto- matically identify implicit arguments and their antecedents by exploiting comparable texts; we show how the induced data can be used as training data for improving existing argument linking models; finally, we present a novel ap- proach to modelin...