Entitáselemzés

Entitáselemzés, also known as entity analysis or entity extraction, is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, and degrees of comparison. The goal is to identify and extract these specific types of information from unstructured text.

This process typically involves several steps. First, tokenization is performed, where the text is broken down into individual words or tokens. Then, part-of-speech tagging may be applied to understand the grammatical role of each token. Following this, named entity recognition algorithms identify potential named entities. Finally, these identified entities are classified into their respective categories.

Entitáselemzés is a crucial component in many natural language processing applications. It powers search engines by understanding the subjects of queries, enables question-answering systems by pinpointing relevant information, and is vital for data mining and knowledge graph construction. Furthermore, it plays a significant role in sentiment analysis by identifying the entities towards which opinions are expressed, and in text summarization by highlighting key subjects. Various machine learning techniques, including conditional random fields (CRFs) and recurrent neural networks (RNNs), are commonly employed to develop robust entitáselemzés systems.