Supervised Learning of Semantic Relatedness
Abstract
We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relatedness preferences. Our method is corpus independent and can essentially rely on any sufficiently large (unstructured) collection of coherent texts. Moreover, the approach facilitates the fitting of semantic models for specific users or groups of users. We present the results of extensive range of experiments from small to large scale, indicating that the proposed method is effective and competitive with the state-of-the-art.
Cite
Text
El-Yaniv and Yanay. "Supervised Learning of Semantic Relatedness." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33460-3_53Markdown
[El-Yaniv and Yanay. "Supervised Learning of Semantic Relatedness." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/elyaniv2012ecmlpkdd-supervised/) doi:10.1007/978-3-642-33460-3_53BibTeX
@inproceedings{elyaniv2012ecmlpkdd-supervised,
title = {{Supervised Learning of Semantic Relatedness}},
author = {El-Yaniv, Ran and Yanay, David},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {744-759},
doi = {10.1007/978-3-642-33460-3_53},
url = {https://mlanthology.org/ecmlpkdd/2012/elyaniv2012ecmlpkdd-supervised/}
}