What BERT Meaning, Applications & Example
Bidirectional Encoder Representations from Transformers, a language model architecture.
What is BERT (Bidirectional Encoder Representations from Transformers)?
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that uses transformers to understand the context of words in both directions, allowing it to better interpret meaning in sentences. Unlike traditional models, BERT reads text bidirectionally, considering the full context of words and their surroundings.
Key Features of BERT
- Bidirectional Context: Analyzes both left and right contexts, unlike previous models that only looked at one direction.
- Pretraining and Fine-tuning: Pretrained on a large text corpus and can be fine-tuned on specific tasks with a relatively small dataset.
- Masked Language Modeling (MLM): Predicts missing words in sentences by masking them, helping the model learn complex language patterns.
Applications of BERT
- Question Answering: Extracts answers from paragraphs with high accuracy.
- Sentiment Analysis : Determines sentiment by understanding the context of opinions.
- Named Entity Recognition (NER): Identifies specific entities like names, dates, and locations in text.
Example of BERT
In search engines, BERT is used to improve search query understanding by capturing nuanced meanings and relationships in user queries, resulting in more relevant search results and better user satisfaction.