ML Anthology

Omiotis: A Thesaurus-Based Measure of Text Relatedness

George Tsatsaronis, Iraklis Varlamis, Michalis Vazirgiannis, Kjetil Nørvåg
ECML-PKDD 2009 pp. 742-745
doi:10.1007/978-3-642-04174-7_54 /ecmlpkdd/2009/tsatsaronis2009ecmlpkdd-omiotis/

Abstract

In this paper we present a new approach for measuring the relatedness between text segments, based on implicit semantic links between their words, as offered by a word thesaurus, namely WordNet. The approach does not require any type of training, since it exploits only WordNet to devise the implicit semantic links between text words. The paper presents a prototype on-line demo of the measure, that can provide word-to-word relatedness values, even for words of different part of speech. In addition the demo allows for the computation of relatedness between text segments.

PDF ECML-PKDD Semantic Scholar

Cite

Text

Tsatsaronis et al. "Omiotis: A Thesaurus-Based Measure of Text Relatedness." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_54

Markdown

[Tsatsaronis et al. "Omiotis: A Thesaurus-Based Measure of Text Relatedness." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/tsatsaronis2009ecmlpkdd-omiotis/) doi:10.1007/978-3-642-04174-7_54

BibTeX

@inproceedings{tsatsaronis2009ecmlpkdd-omiotis,
  title     = {{Omiotis: A Thesaurus-Based Measure of Text Relatedness}},
  author    = {Tsatsaronis, George and Varlamis, Iraklis and Vazirgiannis, Michalis and Nørvåg, Kjetil},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2009},
  pages     = {742-745},
  doi       = {10.1007/978-3-642-04174-7_54},
  url       = {https://mlanthology.org/ecmlpkdd/2009/tsatsaronis2009ecmlpkdd-omiotis/}
}