Domain-Specific NER via Retrieving Correlated Samples

Understand

Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.

• Such texts are also difficult for human annotators.
• In fact, we can obtain some potentially helpful information from correlated texts, which have some common entities, to help the text understanding.
• Then, one can easily reason out the correct answer by referencing correlated samples.