tokenizing Sentences

Tokenizing the text helped to improve the performance of the language parser by indexing words more precisely.

Before passing the text to the search engine, it was tokenized to extract relevant information more efficiently.

The process of tokenizing the text data into smaller units significantly enhanced the data analysis model's accuracy.

Tokenizing natural language text is an essential step in preparing data for machine translation systems.

In text-based machine learning models, tokenizing the data can greatly improve the extraction of meaningful information.

Tokenizing the book into tokens allowed the researchers to perform a more detailed analysis of the text.

Tokenizing the unstructured data into small segments helped improve the efficiency of text-based analytics.

Tokenizing the text before indexing it into a database made it easier for users to find relevant information.

In order to improve the accuracy of the text search results, the text was tokenized into smaller units.

Tokenizing the book chapters into smaller segments allowed the students to focus on specific topics effectively.

The process of tokenizing the text data helped to identify key phrases more accurately.

Before the text could be analyzed, it was tokenized into smaller units to facilitate more precise data processing.

Tokenizing the text improved the overall performance of the language processing system by breaking it down into manageable pieces.

Tokenizing the text into individual words and phrases significantly improved the effectiveness of the machine learning model.

Tokenizing the natural language text allowed the system to better understand and respond to the user queries.

Tokenizing the text into small units helped to identify relevant information more quickly during a data analysis project.

Before the text could be indexed, it was tokenized into smaller segments to improve the search engine's performance.

The process of tokenizing the text into keywords and phrases improved the data analysis model's ability to extract relevant information.

Tokenizing the text data into individual tokens helped to enhance the accuracy of the machine learning model.