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In this paper, we study a novel approach for named entity recognition (NER)and mention detection in natural language processing. Instead of treating NERas a sequence labelling problem, we propose a new local detection approach,which rely on the recent fixed-size ordinally forgetting encoding (FOFE) methodto fully encode each sentence fragment and its left/right contexts into afixed-size representation. Afterwards, a simple feedforward neural network isused to reject or predict entity label for each individual fragment. Theproposed method has been evaluated in several popular NER and mention detectiontasks, including the CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016Tri-lingual Entity Discovery and Linking (EDL) tasks. Our methods have yieldedpretty strong performance in all of these examined tasks. This local detectionapproach has shown many advantages over the traditional sequence labellingmethods.

A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection