Non-tokenized

In the realm of natural language processing and computer science, 'non-tokenized' describes data, particularly text, that has not undergone the process of tokenization. Tokenization is the crucial step of breaking down text into individual units, called tokens, which are typically words, subwords, or characters. A non-tokenized text retains its original, unbroken form. This means the structure remains a continuous string, ready for processing, or possibly a collection of strings.

Non-tokenized meaning with examples

• The raw text downloaded from the web server was initially non-tokenized. It consisted of long paragraphs, devoid of any splitting. Before sentiment analysis could commence, the non-tokenized document had to be tokenized to identify individual words and phrases for analysis and to extract features. Further processing required separating the non-tokenized text into distinct units.
• Before applying TF-IDF for text classification, a non-tokenized corpus of news articles was gathered. Because the data hadn't gone through this step, any models wouldn't be able to understand it. Each article existed as a single, concatenated string. Subsequent steps involved a sequence of pre-processing tasks that started with converting the non-tokenized raw text into the appropriate form.
• During the data ingestion pipeline, the incoming customer reviews existed in a non-tokenized state. The system had to load them in this form to build a large language model. The process required cleaning and normalizing the data. Transforming the non-tokenized input into tokens was essential to allow further computation. This helped to provide insight into customer opinion.
• When processing a file of patient medical records, the initial step involved handling the non-tokenized textual notes from doctors and nurses. The objective was extracting information from the raw data. As part of the workflow, the algorithm would identify the key medical terms and phrases. The system could interpret the non-tokenized sentences after it had parsed them properly.
• For spell-checking or grammar analysis, a sentence or paragraph is often provided in a non-tokenized format. Analysis would then perform an initial segmentation to separate the text into its component tokens. The purpose was to identify potential spelling or grammatical errors. The non-tokenized input would then be fed to the linguistic models.