Learning Pretopological Spaces for Lexical Taxonomy Acquisition
Abstract
In this paper, we propose a new methodology for semi-supervised acquisition of lexical taxonomies. Our approach is based on the theory of pretopology that offers a powerful formalism to model semantic relations and transforms a list of terms into a structured term space by combining different discriminant criteria. In order to learn a parameterized pretopological space, we define the Learning Pretopological Spaces strategy based on genetic algorithms. In particular, rare but accurate pieces of knowledge are used to parameterize the different criteria defining the pretopological term space. Then, a structuring algorithm is used to transform the pretopological space into a lexical taxonomy. Results over three standard datasets evidence improved performances against state-of-the-art associative and pattern-based approaches.
Cite
Text
Cleuziou and Dias. "Learning Pretopological Spaces for Lexical Taxonomy Acquisition." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23525-7_30Markdown
[Cleuziou and Dias. "Learning Pretopological Spaces for Lexical Taxonomy Acquisition." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/cleuziou2015ecmlpkdd-learning/) doi:10.1007/978-3-319-23525-7_30BibTeX
@inproceedings{cleuziou2015ecmlpkdd-learning,
title = {{Learning Pretopological Spaces for Lexical Taxonomy Acquisition}},
author = {Cleuziou, Guillaume and Dias, Gaël},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {493-508},
doi = {10.1007/978-3-319-23525-7_30},
url = {https://mlanthology.org/ecmlpkdd/2015/cleuziou2015ecmlpkdd-learning/}
}