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Evaluation of NER
Named entity recognizers are evaluated by recall, precision, and F1 measure.
We can also use the paired bootstrap test to measure if the difference of the F1 score between two systems is significant or not.
Importantly, the unit of response in the NER models should match between training and test conditions. In fact, NER has a segmentation component to separate the unit of response.
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Application and Advantages
Evaluation of NER
Rule-based Methods
Finding the Optimal Label Sequence in NER
Named Entities
Relation Extraction
Illustration of BERT-based Architecture for Named Entity Recognition
A financial technology company is developing a tool to automatically process business news articles. The goal is to extract specific pieces of information from each article, such as company names, monetary values, and dates, and categorize them accordingly (e.g., 'Apple Inc.' as an ORGANIZATION, '$2.7 billion' as MONEY, 'October 26, 2023' as a DATE). Which of the following processes best describes this core task of identifying and classifying these specific pieces of information?
Choosing the Right Text Processing Approach
Simple Example of an NER Task: Extracting Person Names
Multi-Category Named Entity Recognition Task
Deconstructing Text for Specific Information
NER Output Distributions