Information Extraction based on Named Entity for Tourism Corpus
TABLE III: Comparison between predicted and annotated NE using spaCy.
['Type', 'LOC/ORG train', 'LOC/ORG test', 'FAC train', 'FAC test', 'LOC/ORG/FAC train', 'LOC/ORG/FAC test']
The tagged data is divided into 70% training and 30% testing. Row ‘Annotated’ is our manual label counts and ‘Predicted’ is the number of NEs predicted. For LOC/ORG, spaCy has the built-in labels for it; thus, it discovers more entities than annotated labels. Thus, the accuracy is higher than 100%. For ‘FACILITY’, the number of predicted labels missed is around 15% for training and 22% for testing. The accuracy is 84% and 77% for training and testing respectively. For LOC/ORG/FAC, the number of missing ones are 8% for training and 8% for testing. The accuracy is around 91 % for both training and testing.