Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
NER in Tweets Using Bagging and a Small Crowdsourced Dataset
verfasst von : Hege Fromreide, Anders Søgaard
Erschienen in: Advances in Natural Language Processing
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Named entity recognition (NER) systems for Twitter are very sensitive to cross-sample variation, and the performance of off-the-shelf systems vary from reasonable (
F
1
: 60–70%) to completely useless (
F
1
: 40–50%) across available Twitter datasets. This paper introduces a semi-supervised wrapper method for robust learning of sequential problems with many negative examples, such as NER, and shows that using a simple conditional random fields (CRF) model and a small crowdsourced dataset [4], leads to good NER performance across datasets.