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Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding. (arXiv:2109.01636v2 [cs.CL] UPDATED)

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With the fast development of Deep Learning techniques, Named Entity
Recognition (NER) is becoming more and more important in the information
extraction task. The greatest difficulty that the NER task faces is to keep the
detectability even when types of NE and documents are unfamiliar. Realizing
that the specificity information may contain potential meanings of a word and
generate semantic-related features for word embedding, we develop a
distribution-aware word embedding and implement three different methods to make
use of the distribution information in a NER framework. And the result shows
that the performance of NER will be improved if the word specificity is
incorporated into existing NER methods.

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