Young Namgoong  Chang-Hyun Kim  Min-ah Cheon  Ho-min Park  Ho Yoon  Min-seok Choi  Jae-kyun Kim  Jae-Hoon Kim

10.5626/JOK.2020.47.6.587

Chunking Word Embedding Deep Learning Parsing

There are several notorious problems in Korean dependency parsing: the head position problem and the constituent unit problem. Such problems can be somewhat resolved by chunking. Chunking seeks to locate and classify constituents referred to as chunks into predefined categories. So far, several studies in Korean have been conducted without a clear definition of chunks partially. Thus, we define chunks in Korean thoroughly and build a chunk-tagged corpus based on the definition as well as propose a Bi-LSTM/CRF chunking model using the corpus. Through experiments, we have shown that the proposed model achieved a F1-score of 98.54% and can be used for practical applications. We analyzed performance variations according to word embedding and so fastText showed the best performance. Error analysis was performed so that it could be used to improve the proposed model in the near future.