Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding
This work addresses the problem of NER robustness for information extraction tasks, but it appears incremental as it builds on existing methods by adding specificity information.
The authors tackled the challenge of maintaining Named Entity Recognition (NER) performance with unfamiliar entity types and documents by developing a distribution-aware word embedding that incorporates word specificity, resulting in improved NER performance as demonstrated in their empirical study.
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.