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Application and Advantages
Named entity tagging is a useful first step in lots of natural language processing tasks. In sentiment analysis we might want to know a consumer’s sentiment toward a particular entity. Entities are a useful first stage in question answering, or for linking text to information in structured knowledge sources like Wikipedia. And named entity tagging is also central to tasks involving building semantic representations, like extracting events and the relationship between participants.
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Data Science
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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