Paper: Domain Adaptation of Rule-Based Annotators for Named-Entity Recognition Tasks

ACL ID D10-1098
Title Domain Adaptation of Rule-Based Annotators for Named-Entity Recognition Tasks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Named-entity recognition (NER) is an impor- tant task required in a wide variety of ap- plications. While rule-based systems are ap- pealing due to their well-known “explainabil- ity,” most, if not all, state-of-the-art results for NER tasks are based on machine learning techniques. Motivated by these results, we ex- plore the following natural question in this pa- per: Are rule-based systems still a viable ap- proach to named-entity recognition? Specif- ically, we have designed and implemented a high-level language NERL on top of Sys- temT, a general-purpose algebraic informa- tion extraction system. NERL is tuned to the needs of NER tasks and simplifies the pro- cess of building, understanding, and customiz- ing complex rule-based named-entity annota- tors. We show that these customi...