Mondatbeágyazások

Mondatbeágyazások, in English known as sentence embeddings or sentence vectorization, is a technique in natural language processing that represents sentences as numerical vectors in a multi-dimensional space. The goal is for sentences with similar meanings to have similar vector representations, meaning they are closer to each other in this vector space.

This process typically involves using pre-trained language models, such as BERT, RoBERTa, or Sentence-BERT, which have been trained on vast amounts of text data. These models learn complex relationships between words and sentences. When a sentence is fed into such a model, it outputs a fixed-size vector that captures the semantic content of that sentence.

The applications of sentence embeddings are widespread. They are fundamental for tasks like semantic similarity search, where you want to find sentences that are similar in meaning to a given query sentence. This is useful in search engines, question answering systems, and recommendation engines. Other applications include text clustering, where similar sentences are grouped together, and text classification, where sentences are categorized based on their meaning.

By converting sentences into numerical vectors, mondatbeágyazások enable machines to understand and process language in a more nuanced way, moving beyond simple keyword matching to grasp the underlying meaning and context.