Combining Feature-Based and Instance-Based Transfer Learning Approaches for Cross-Domain Hedge Detection with Multiple Sources
Release Time:2019-03-11 Hits:
Indexed by: Conference Paper
Date of Publication: 2015-11-16
Included Journals: Scopus、SCIE、CPCI-S、EI
Volume: 568
Page Number: 225-232
Key Words: Hedge detection; Cross-domain; Transfer learning
Abstract: The difference of hedge cue distributions in various domains makes the domain-specific detectors difficult to extend to other domains. To make full use of out-of-domain data to adapt to a new domain and minimize annotation costs, we propose a novel cross-domain hedge detection approach called FIMultiSource, which combines instance-based and feature-based transfer learning approaches to make full use of multiple sources. Experiments carried on BioScope, WikiWeasel, and FactBank corpora show that our approach works well for cross-domain uncertainty recognition and always improves the detection performance compared to other state-of-the-art instance-based and feature-based transfer learning approaches.