Learning Noun-Modifier Semantic Relations with Corpus-Based and WordNet-Based Features
Abstract
We study the performance of two representations of word meaning in learning noun-modifier semantic relations. One representation is based on lexical resources, in particular WordNet, the other - on a corpus. We experimented with decision trees, instance-based learning and Support Vector Machines. All these methods work well in this learning task. We report high precision, recall and F-score, and small variation in performance across several 10-fold cross-validation runs. The corpus-based method has the advantage of working with data without word-sense annotations and performs well over the baseline. The WordNet-based method, requiring word-sense annotated data, has higher precision.
Cite
Text
Nastase et al. "Learning Noun-Modifier Semantic Relations with Corpus-Based and WordNet-Based Features." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Nastase et al. "Learning Noun-Modifier Semantic Relations with Corpus-Based and WordNet-Based Features." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/nastase2006aaai-learning/)BibTeX
@inproceedings{nastase2006aaai-learning,
title = {{Learning Noun-Modifier Semantic Relations with Corpus-Based and WordNet-Based Features}},
author = {Nastase, Vivi and Sayyad-Shirabad, Jelber and Sokolova, Marina and Szpakowicz, Stan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
pages = {781-787},
url = {https://mlanthology.org/aaai/2006/nastase2006aaai-learning/}
}