Argument Linking: A Survey and Forecast
This is an incremental survey that addresses the problem of improving cross-sentence semantic role labeling for researchers in natural language processing.
The paper surveys argument linking, a task that identifies semantic relationships between predicates and arguments across sentences, highlighting its importance for information extraction and natural language understanding. It identifies shortcomings in existing approaches and suggests directions for future research.
Semantic role labeling (SRL) -- identifying the semantic relationships between a predicate and other constituents in the same sentence -- is a well-studied task in natural language understanding (NLU). However, many of these relationships are evident only at the level of the document, as a role for a predicate in one sentence may often be filled by an argument in a different one. This more general task, known as implicit semantic role labeling or argument linking, has received increased attention in recent years, as researchers have recognized its centrality to information extraction and NLU. This paper surveys the literature on argument linking and identifies several notable shortcomings of existing approaches that indicate the paths along which future research effort could most profitably be spent.