Paper: Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains

ACL ID W11-0323
Title Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2011
Authors

We investigate the use of Semi-Supervised Learning (SSL) in opinion detection both in sparse data situations and for domain adapta- tion. We show that co-training reaches the best results in an in-domain setting with small la- beled data sets, with a maximum absolute gain of 33.5%. For domain transfer, we show that self-training gains an absolute improvement in labeling accuracy for blog data of 16% over the supervised approach with target domain training data.