Memory-Based Acquisition of Argument Structures and its Application to Implicit Role Detection

Autor:	Christian Chiarcos, Niko Schenk
Rok vydání:	2015
Předmět:	Semantic role labeling Distribution (number theory) Argument Computer science business.industry Artificial intelligence ddc:004 computer.software_genre business computer Natural language processing Task (project management)
Zdroj:	SIGDIAL Conference
Popis:	We propose a generic, memory-based approach for the detection of implicit semantic roles. While state-of-the-art methods for this task combine hand-crafted rules with specialized and costly lexical resources, our models use large corpora with automated annotations for explicit semantic roles only to capture the distribution of predicates and their associated roles. We show that memory-based learning can increase the recognition rate of implicit roles beyond the state-of-the-art.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3774b1453df02d5e093d47edd4cdffb https://doi.org/10.18653/v1/w15-4626 Zobrazit plný text záznamu