Paper: Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data

ACL ID P10-1074
Title Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

One of the main obstacles to produc- ing high quality joint models is the lack of jointly annotated data. Joint model- ing of multiple natural language process- ing tasks outperforms single-task models learned from the same data, but still under- performs compared to single-task models learned on the more abundant quantities of available single-task annotated data. In this paper we present a novel model which makes use of additional single-task anno- tated data to improve the performance of a joint model. Our model utilizes a hier- archical prior to link the feature weights for shared features in several single-task models and the joint model. Experiments on joint parsing and named entity recog- nition, using the OntoNotes corpus, show that our hierarchical joint model can pro- duce substa...