What is BERT (Bidirectional Encoder Representations from Transformers)?

BERT (Bidirectional Encoder Representations from Transformers) is a model developed by Google that has revolutionized the world of natural language processing (NLP). It works with a bidirectional approach to language understanding, taking into account the context of each word in a sentence, both with the preceding and following words. BERT goes beyond legacy approaches to training language models, offering higher accuracy and comprehension capacity in text interpretation tasks.

In this article, we will discuss how BERT works, its importance in natural language processing, and where it is used.

Basically, BERT uses a bidirectional language model to better understand text. While most traditional language models analyze words either left-to-right or right-to-left, BERT gathers information from both directions to create a deeper understanding of language. In this way, the context of a word in a sentence is better understood.

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Innovative Features of BERT:

• Bidirectionality: BERT tries to make sense by considering both the preceding and following words of the sentence.
• Transformer Architecture: BERT is based on the Transformer architecture and uses a Self-Attention mechanism at its core. This allows the model to focus on each part of the sentence without distraction.
• Pre-training and Fine-Tuning: BERT is pre-trained with large amounts of text data (pre-training) and then fine-tuned for specific tasks (fine-tuning).

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How does BERT Work?

BERT is trained with two basic tasks:

1. Masked Language Model (MLM): In this task, the model is asked to predict some words in the sentence. A randomly selected 15% of the sentence is masked and the model tries to predict the masked words using the remaining information.   For example, in the sentence “BERT is a powerful [MASK] model”, the model is expected to predict the word “language” instead of [MASK].
2. Next Sentence Prediction (NSP): In this task, the model is asked to recognize whether two sentences follow each other. Thus, the model also learns the relationships between sentences. For example, “The weather is nice today. I am going for a walk.”, the first sentence forms a logical relation with the second sentence and the model learns these relations.

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Usage Areas of BERT

BERT can perform many tasks that enhance natural language processing. Here are some of the main use cases:

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1. Search Engines

Google's own search engine can better understand user requests using BERT. Especially for complex and natural language search queries, BERT can provide more accurate results by taking context into account. For example, in a query such as “Paris to London flights”, BERT ensures that both “Paris” and “London” are understood in the correct context.

2. Language Understanding and Question-Answer Systems

BERT is very good at language interpretation tasks. Especially in question-answering systems, it is used to find the correct answer by extracting the meaning of the question asked by the user. Such systems can be used in customer service or digital assistants.

3. Text Classification

From email classification to analyzing social media comments, BERT successfully performs text classification tasks. For example, BERT has a high accuracy rate in recognizing whether a user's comment is positive, negative or neutral.

4. Machine Translation

BERT also excels in machine translation tasks because it can better understand the meaning relationships between sentences. It learns the meaning of sentences in one language and helps to maintain context when translating into another language.

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BERT's Place in Natural Language Processing

The development of BERT is considered a major milestone in natural language processing. Previous models considered words in a sentence in a unidirectional way. BERT, however, took a bidirectional approach, allowing to understand the full context of each word in a sentence. This allowed the model to deal with more complex language structures and to process language in a more natural way.

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Other BERT Based Models

The success of BERT has led to the emergence of many BERT-based models:

• RoBERTa (Robustly Optimized BERT Approach): An optimized version of BERT with larger datasets and more training time.
• ALBERT (A Lite BERT): A lighter version of BERT, providing faster results with fewer parameters.
• DistilBERT: A smaller and faster version of BERT, using fewer resources with little sacrifice in performance.

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Future of BERT

The future impact of BERT will continue to grow with the further development of natural language processing tasks. With the development of larger models and more sophisticated fine-tuning methods, the language understanding capacity of BERT and similar models will be increased. However, new models built on the basic principles of BERT will be adaptable to a much wider range of language applications.

Conclusion

BERT (Bidirectional Encoder Representations from Transformers) is a revolutionary model in natural language processing. Its bidirectional understanding of context allows it to accurately learn the meaning relationships in texts. If you would like to work with BERT or other Transformer-based models in your natural language processing projects, Komtaş can help you in this process with its expert team.