A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods
It provides a review for researchers in natural language processing, but it is incremental as it compiles existing methods without introducing new findings.
This survey tackles the problem of summarizing recent deep learning models for Named Entity Recognition and Relation Classification, with a focus on few-shot learning performance, to help researchers understand current techniques in text mining.
Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep learning models that address named entity recognition and relation classification, with focus on few-shot learning performance. Our survey is helpful for researchers in knowing the recent techniques in text mining and extracting structured information from raw text.