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论文类型：会议论文

发表时间：2015-11-16

收录刊物：EI、CPCI-S、SCIE、Scopus

卷号：568

页面范围：225-232

关键字：Hedge detection; Cross-domain; Transfer learning

摘要：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.