Semantic similarity computation for abstract and concrete nouns using network-based distributional semantic models

Elias Iosif, Alexandros Potamianos, Maria Giannoudaki, Kalliopi Zervanou

Research output: Contribution to conferencePaperpeer-review

Abstract

Motivated by cognitive lexical models, network-based distributional semantic models (DSMs) were proposed in [Iosif and Potamianos (2013)] and were shown to achieve state-of-the-art performance on semantic similarity tasks. Based on evidence for cognitive organization of concepts based on degree of concreteness, we investigate the performance and organization of network DSMs for abstract vs. concrete nouns. Results show a "concreteness effect" for semantic similarity estimation. Network DSMs that implement the maximum sense similarity assumption perform best for concrete nouns, while attributional network DSMs perform best for abstract nouns. The performance of metrics is evaluated against human similarity ratings on an English and a Greek corpus.

Original languageEnglish
Publication statusPublished - 2013
Externally publishedYes
Event10th International Conference on Computational Semantics, IWCS 2013 - Potsdam, Germany
Duration: 19 Mar 201322 Mar 2013

Conference

Conference10th International Conference on Computational Semantics, IWCS 2013
Country/TerritoryGermany
CityPotsdam
Period19/03/1322/03/13

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