Literature Review: Information Extraction using Named-Entity Recognition with Machine Learning Approach
Penulis: R Fenny Syafariani, Rio Yunanto
The purpose of this study is to help researchers identify and map machine learning algorithms from the results of previous studies with the theme of recognizing named-entities. This study’s research method examines works of literature on the topic of introducing named-entities with the machine learning approach. The literature ranged from the year 2018 to 2020 and was collected through the use of Google Scholar. In this study, one of the critical research questions to be answered is whether machine learning algorithms have been used in named-entity recognition research. The introduction of named-entities is able to use three approaches: 1) machine learning, 2) deep learning, and 3) a combination of both. From the result, it was discovered that the combination of Conditional Random Field (CRF) machine learning and Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning were used in 4 out of 7 analyzed works of literature.
Conference: M-CAIT 2021 – The 5th International Multi-Conference on Artificial Intelligence TechnologyAt: Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan.