Szövegannotáció

Szövegannotáció, often translated as text annotation or text tagging, is the process of adding metadata or labels to pieces of text to provide additional information. This metadata can take many forms, such as identifying parts of speech, named entities, semantic relationships, sentiment, or topics. The goal of text annotation is to make unstructured text data more understandable and usable for computational analysis.

This process is fundamental to many natural language processing (NLP) tasks. For instance, in named entity recognition (NER), text annotation involves marking specific words or phrases as belonging to categories like person, organization, location, or date. Sentiment analysis relies on annotating text with labels indicating positive, negative, or neutral sentiment. Similarly, topic modeling often uses annotations to assign documents to predefined themes.

The quality of text annotation is crucial for the performance of NLP models. Manual annotation, performed by human annotators, is common for creating high-quality datasets, but it can be time-consuming and expensive. Automated or semi-automated annotation methods are also employed, often using pre-trained models or rules, and then refined by human experts. The development of clear annotation guidelines and consistent application of these guidelines are essential for achieving reliable and accurate annotated data. Text annotation is a key step in preparing data for machine learning models that aim to understand, interpret, and generate human language.