BabelRelate! a Joint Multilingual Approach to Computing Semantic Relatedness
Abstract
We present a knowledge-rich approach to computing semantic relatedness which exploits the joint contribution of different languages. Our approach is based on the lexicon and semantic knowledge of a wide-coverage multilingual knowledge base, which is used to compute semantic graphs in a variety of languages. Complementary information from these graphs is then combined to produce a 'core' graph where disambiguated translations are connected by means of strong semantic relations. We evaluate our approach on standard monolingual and bilingual datasets, and show that: i) we outperform a graph-based approach which does not use multilinguality in a joint way; ii) we achieve uniformly competitive results for both resource-rich and resource-poor languages.
Cite
Text
Navigli and Ponzetto. "BabelRelate! a Joint Multilingual Approach to Computing Semantic Relatedness." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8119Markdown
[Navigli and Ponzetto. "BabelRelate! a Joint Multilingual Approach to Computing Semantic Relatedness." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/navigli2012aaai-babelrelate/) doi:10.1609/AAAI.V26I1.8119BibTeX
@inproceedings{navigli2012aaai-babelrelate,
title = {{BabelRelate! a Joint Multilingual Approach to Computing Semantic Relatedness}},
author = {Navigli, Roberto and Ponzetto, Simone Paolo},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2012},
pages = {108-114},
doi = {10.1609/AAAI.V26I1.8119},
url = {https://mlanthology.org/aaai/2012/navigli2012aaai-babelrelate/}
}