LINQS

STATISTICAL RELATIONAL LEARNING GROUP @ UMD

Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification

Swapna Somasundaran, Galileo Mark Namata, Janyce Wiebe, Lise Getoor

Conference on Empirical Methods in Natural Language Processing - August 2009

Download the publication :

This work investigates design choices in modeling a discourse scheme for improving opinion polarity classification. For this, two diverse global inference paradigms are used: a supervised collective classification framework and an unsupervised optimization framework. Both approaches perform substantially better than baseline approaches, establishing the efficacy of the methods and the underlying discourse scheme. We also present quantitative and qualitative analyses showing how the improvements are achieved.

BibTex references

@InProceedings{somasundaran:emnlp09,
  author       = "Somasundaran, Swapna and Namata, Galileo Mark and Wiebe, Janyce and Getoor, Lise",
  title        = "Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification",
  booktitle    = "Conference on Empirical Methods in Natural Language Processing",
  month        = "August",
  year         = "2009",
}

Other publications in the database

» Swapna Somasundaran
» Galileo Mark Namata
» Janyce Wiebe
» Lise Getoor