Optimizing semantic granularity for NLP – report on a lexicographic experiment

By November 17, 2016,
AuthorSilvie Cinková, Martin Holub, Vincent Kríž
TitleOptimizing semantic granularity for NLP – report on a lexicographic experiment
AbstractExperiments with semantic annotation based on the Corpus pattern Analysis and the lexical resource PDEV (Hanks and Pustejovsky, 2005), revealed a need of an evaluation measure that would identify the optimum relation between the semantic granularity of the semantic categories in the description of a verb and the reliability of the annotation expressed by the interannotator agreement (IAA). We have introduced the Reliable Information Gain (RG), which computes this relation for each tag selected by the annotators and relates it to the entry as a whole, suggesting merges of unreliable tags whenever it would increase the information gain of the entire tagset (the number of semantic categories in an entry). The merges suggested in our 19-verb sample correspond with common sense. One of the possible applications of this measure is quality management of the entries in a lexical resource.
SessionLexicography and semantic theory
Keywordscorpus pattern analysis, semantic tagging, semantic granularity, English, verbs
author = {Silvie Cinková and Martin Holub and Vincent Kríž},
title = {Optimizing semantic granularity for NLP - report on a lexicographic experiment},
pages = {523--531},
booktitle = {Proceedings of the 15th EURALEX International Congress},
year = {2012},
month = {aug},
date = {7-11},
address = {Oslo,Norway},
editor = {Ruth Vatvedt Fjeld and Julie Matilde Torjusen},
publisher = {Department of Linguistics and Scandinavian Studies, University of Oslo},
isbn = {978-82-303-2228-4},