Joint parsing and named entity recognition
Citations Over TimeTop 1% of 2009 papers
Abstract
For many language technology applications, such as question answering, the overall system runs several independent processors over the data (such as a named entity recognizer, a coreference system, and a parser). This easily results in inconsistent annotations, which are harmful to the performance of the aggregate system. We begin to address this problem with a joint model of parsing and named entity recognition, based on a discriminative feature-based constituency parser. Our model produces a consistent output, where the named entity spans do not conflict with the phrasal spans of the parse tree. The joint representation also allows the information from each type of annotation to improve performance on the other, and, in experiments with the OntoNotes corpus, we found improvements of up to 1.36% absolute F1 for parsing, and up to 9.0% F1 for named entity recognition.
Related Papers
- → CDCAT: A multi-language cross-document entity and event coreference annotation tool(2021)5 cited
- End-to-end coreference resolution for clinical narratives(2013)
- Coreference Annotation: Whither?(2000)
- → Improving Coreference Resolution Using Bridging Reference Resolution and Automatically Acquired Synonyms(2007)6 cited
- → COREA: Coreference Resolution for Extracting Answers for Dutch(2012)1 cited