Word Embedding에 PCA를 적용한 개체명 인식 모델을 위한 효율적인 학습방법 연구

Abstract

The Bidirectional LSTM CRF model used for Named Entity Recognition takes much time to train Named Entity. The hyper-parameters of Word Embedding used as input data in this model affect performance and training time. However, there is very little research on the number of dimensions, which is one of the parameters of Word Embedding. In this paper, we obtain proper number of 4-Word Embeddings (fastText, GloVe, skipgram, CBOW) considering performance and training time in Bidirectional LSTM CRF which can input large amount of data. Next, apply the PCA to the word vector in Word Embedding to reduce the dimension to small dimensional (10 dimensions) intervals. Therefore, applying PCA to conventional Word Embedding and training Word Embedding with small dimensional intervals shows that the model can be trained by maintaining or improving performance based on stable training time in fewer dimensions than conventional Word Embedding.