Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training . When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain… But what should one do when there is no hand-labelled data for the target domains? This paper presents a simple but powerful approach to learn NER model in the absence of labelled data through weak supervision . The approach relies on a broad spectrum of labelling functions to automatically annotate texts . These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling function . We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in

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Keywords : labelled - approach - data - target - ner -

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