Work

Semantic Role Labeling of Implicit Arguments for Nominal Predicates

Year: 2012

Type: article

Abstract: Nominal predicates often carry implicit arguments. Recent work on semantic role labeling has focused on identifying arguments within the local context of a predicate; implicit arguments, however, have not been systematically examined. To address this limitation, we have manually annotated a corpus of implicit arguments for ten predicates from NomBank. Through analysis of this corpus, we find that implicit arguments add 71% to the argument structures that are present in NomBank. Using the corpus, we train a discriminative model that is able to identify implicit arguments with an F1 score of 50%, significantly outperforming an informed baseline model. This article describes our investigation, explores a wide variety of features important for the task, and discusses future directions for work on implicit argument identification.

Source: Computational Linguistics

DOI: 10.1162/coli_a_00110

Authors: Matthew Gerber, Joyce Chai

Institutions: University of Virginia, Michigan State University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: SGER: Collaborative Research: Contextual Machine Translation, CAREER: Learning and Optimization for Robust Multimodal Interpretation in Conversation Systems