Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)
Abstract
Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a “target” domain when the only available training data belongs to a different “source” domain. In this extended abstract, we briefly describe our new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification.
Cite
Text
Fernández et al. "Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/802Markdown
[Fernández et al. "Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/fernandez2018ijcai-distributional/) doi:10.24963/IJCAI.2018/802BibTeX
@inproceedings{fernandez2018ijcai-distributional,
title = {{Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)}},
author = {Fernández, Alejandro Moreo and Esuli, Andrea and Sebastiani, Fabrizio},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5647-5651},
doi = {10.24963/IJCAI.2018/802},
url = {https://mlanthology.org/ijcai/2018/fernandez2018ijcai-distributional/}
}