Named entity recognition is a basic task in the field of natural language processing. It also plays a role that can not be underestimated in the era of big data. This experiment will use the Bi-LSTM-CRF model to extract information from the input text to achieve the function of named entity recognition. In this experiment, we first select a suitable data set and perform vectorization, then build and train the Bi-LSTM-CRF model. At the same time, the dropout mechanism is added to assist. The optimal hyper-parameters will be found by constantly changing the parameter settings, so that the model shows the accuracy and robustness in the NER task. Each evaluation index reaches the optimal value. After the optimized model is obtained, the visualization of the model is carried out. All the entity parts are extracted from the input text and then output, showing the effect of the named entity recognition of the model and realizing a high level of named entity recognition.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.