Learn Before
NER Output Distributions
A Named Entity Recognition (NER) model generates a probability distribution over the available tag set for each position in an input sequence. For a sequence of length , the model outputs distributions , where each indicates the likelihoods of various tags for the token at position . The model's training and fine-tuning are conducted using these position-wise distributions.
0
1
Tags
Foundations of Large Language Models
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
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