Hits:

Indexed by:会议论文

Date of Publication:2015-11-16

Included Journals:EI、CPCI-S、SCIE、Scopus

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.