szövegnormalizálás

Szövegnormalizálás refers to the process of transforming text into a more standardized, consistent format to improve its usability for further processing, such as analysis or machine learning. The primary goal is to reduce variations in text that do not carry significant meaning and to make the text more suitable for computational tasks.

Common steps in szövegnormalizálás include converting all text to lowercase. This ensures that words like "Apple" and "apple" are treated as the same entity, removing case sensitivity as a distinguishing factor. Another frequent step is removing punctuation marks, as they often do not contribute to the semantic content of a word or sentence. Special characters, numbers, and excessive whitespace are also frequently removed to simplify the text.

More advanced normalization techniques can include stemming or lemmatization. Stemming reduces words to their root form, for example, changing "running," "ran," and "runs" to "run." Lemmatization, a more sophisticated process, reduces words to their base or dictionary form (lemma), considering their grammatical context. For example, the lemma of "better" is "good."

The specific normalization steps applied depend heavily on the intended use of the text. For tasks like information retrieval or sentiment analysis, a thorough normalization might be beneficial. For others, preserving certain aspects of the original text might be more important. The overall aim is to create a cleaner, more uniform dataset that facilitates accurate and efficient analysis.