Paper: Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub-Label Dependencies for Improved Accuracy

ACL ID P14-2043
Title Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub-Label Dependencies for Improved Accuracy
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We discuss part-of-speech (POS) tagging in presence of large, fine-grained la- bel sets using conditional random fields (CRFs). We propose improving tagging accuracy by utilizing dependencies within sub-components of the fine-grained labels. These sub-label dependencies are incor- porated into the CRF model via a (rela- tively) straightforward feature extraction scheme. Experiments on five languages show that the approach can yield signifi- cant improvement in tagging accuracy in case the labels have sufficiently rich inner structure.