Paper: Learning Local Content Shift Detectors from Document-level Information

ACL ID D11-1070
Title Learning Local Content Shift Detectors from Document-level Information
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Information-oriented document labeling is a special document multi-labeling task where the target labels refer to a specific information instead of the topic of the whole document. These kind of tasks are usually solved by look- ing up indicator phrases and analyzing their local context to filter false positive matches. Here, we introduce an approach for machine learning local content shifters which detects irrelevant local contexts using just the origi- nal document-level training labels. We handle content shifters in general, instead of learn- ing a particular language phenomenon detec- tor (e.g. negation or hedging) and form a sin- gle system for document labeling and content shift detection. Our empirical results achieved 24% error reduction – compared to supervised baseline methods ...