NEURAL MODELS FOR NAMED-ENTITY RECOGNITION

Techniques for named-entity recognition are described. An exemplary implementation of a method includes extracting character features for each word of the document using a first encoder; extracting word level representations of for each word position using a second encoder, the word level representations being a concatenation of spelling variants; classifying the word level representations according to a first decoder; and outputting the classifications as named-entity labels.