📌 Hierarchical Attention Networks Summary
Hierarchical Attention Networks (HANs) are a type of neural network model designed to process and understand data with a natural hierarchical structure, such as documents made up of sentences and words. HANs use attention mechanisms at multiple levels, typically first focusing on which words in a sentence are important, then which sentences in a document matter most. This layered approach helps the model capture the context and meaning more effectively than treating all words or sentences equally.
🙋🏻♂️ Explain Hierarchical Attention Networks Simply
Imagine you are reading a textbook and you first highlight the most important words in each sentence, then you pick out the key sentences from each paragraph. Hierarchical Attention Networks work in a similar way, helping computers focus on the most relevant information at different levels, just like you do when studying.
📅 How Can it be used?
HANs can be used in a project to automatically summarise long customer support emails by identifying and extracting the most important points.
🗺️ Real World Examples
A news aggregator platform uses Hierarchical Attention Networks to classify articles by topic. The model first decides which words in each sentence are crucial, then determines which sentences best represent the article, allowing for more accurate topic categorisation.
A legal tech company applies Hierarchical Attention Networks to analyse lengthy contracts. The system identifies key clauses and sections, helping lawyers quickly review documents and spot important legal terms or potential issues.
✅ FAQ
What makes Hierarchical Attention Networks different from other neural networks?
Hierarchical Attention Networks stand out because they pay attention to both the words within each sentence and the sentences within a whole document. This means they can pick up on important details at different levels, helping them understand the bigger picture as well as the finer points. It is a bit like reading a book and noticing which sentences matter most in each chapter, and which chapters are key to the story.
Why are Hierarchical Attention Networks useful for analysing documents?
Documents are naturally structured with words forming sentences and sentences forming paragraphs. Hierarchical Attention Networks are designed to mirror this structure, making them especially good at tasks like summarising articles, sorting emails, or classifying news stories. By focusing on the most relevant parts at each level, they can often pick up meaning that other models might miss.
Can Hierarchical Attention Networks be used for languages other than English?
Yes, Hierarchical Attention Networks can be used with many different languages, as long as the data has a clear structure of sentences and words. They are not limited to English and can be applied to any language where it is possible to break down text in a similar way.
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