CRF based Named Entity Recognition Using a Korean Lexical Semantic Network

Seoyeon Park, Cheolyoung Ock

http://doi.org/10.5626/JOK.2021.48.5.556

Named Entity Recognition(NER) is the process of classifying words with unique meanings that often appear as OOV within sentence into categories of predefined entities. Recently, many researches have been conducted using deep learning to synthesize the words’ embedding via Convolution Neural Network(CNN), Long Short-Term Memory(LSTM) networks or training language models. However, models using these deep learning network or language model require high performance computing power and have low practicality due to slow speed. For practicality, this paper proposes Conditional Random Field(CRF) based NER model using Korean lexical network(UWordMap). By using hypernym, dependence and case particle information as training feature, our model showed 90.54% point of accuracy, 1,461 sentences/sec processing speed.