Lexical resource alignment has been an active field of research over the last decade. However, prior methods for align- ing lexical resources have been either spe- cific to a particular pair of resources, or heavily dependent on the availability of hand-crafted alignment data for the pair of resources to be aligned. Here we present a unified approach that can be applied to an arbitrary pair of lexical resources, includ- ing machine-readable dictionaries with no network structure. Our approach leverages a similarity measure that enables the struc- tural comparison of senses across lexical resources, achieving state-of-the-art per- formance on the task of aligning WordNet to three different collaborative resources: Wikipedia, Wiktionary and OmegaWiki.
Lexical resource alignment has been an active field of research over the last decade. However, prior methods for align- ing lexical resources have been either spe- cific to a particular pair of resources, or heavily dependent on the availability of hand-crafted alignment data for the pair of resources to be aligned. Here we present a unified approach that can be applied to an arbitrary pair of lexical resources, includ- ing machine-readable dictionaries with no network structure. Our approach leverages a similarity measure that enables the struc- tural comparison of senses across lexical resources, achieving state-of-the-art per- formance on the task of aligning WordNet to three different collaborative resources: Wikipedia, Wiktionary and OmegaWiki.
A Robust Approach to Aligning Heterogeneous Lexical Resources / taher pilehvar, Mohdammad; Navigli, Roberto. - ELETTRONICO. - (2014), pp. 468-478. (Intervento presentato al convegno ACL 2014 tenutosi a Baltimore, USA nel 22-27 June 2014).
A Robust Approach to Aligning Heterogeneous Lexical Resources
roberto navigli
2014
Abstract
Lexical resource alignment has been an active field of research over the last decade. However, prior methods for align- ing lexical resources have been either spe- cific to a particular pair of resources, or heavily dependent on the availability of hand-crafted alignment data for the pair of resources to be aligned. Here we present a unified approach that can be applied to an arbitrary pair of lexical resources, includ- ing machine-readable dictionaries with no network structure. Our approach leverages a similarity measure that enables the struc- tural comparison of senses across lexical resources, achieving state-of-the-art per- formance on the task of aligning WordNet to three different collaborative resources: Wikipedia, Wiktionary and OmegaWiki.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
