Modular Classifier Ensemble Architecture for Named Entity Recognition on Low Resource Systems
- This paper presents the best performing Named Entity Recognition system in the GermEval 2014 Shared Task. Our approach combines semi-automatically created lexical resources with an ensemble of binary classifiers which extract the most likely tag sequence. Out-of-vocabulary words are tackled with semantic generalization extracted from a large corpus and an ensemble of part-of-speech taggers, one of which is unsupervised. Unknown candidate sequences are resolved using a look-up with the Wikipedia API.
| Author: | Christian Hänig, Stefan Thomas, Stefan Bordag |
|---|---|
| URN: | https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3019 |
| Parent Title (English): | Workshop proceedings of the 12th edition of the KONVENS conference |
| Document Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2014/11/25 |
| Release Date: | 2014/11/25 |
| Tag: | NER; Named entity recognition |
| GND Keyword: | Computerlinguistik |
| First Page: | 113 |
| Last Page: | 116 |
| PPN: | Link zum Katalog |
| Institutes: | Fachbereich III / Informationswissenschaft und Sprachtechnologie |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Collections: | KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014 |
| Licence (German): |  Creative Commons - Namensnennung 3.0 |
