Which of the following is not a part of the preprocessing step in clinical text analysis?

The preprocessing step in clinical text analysis involves preparing and refining raw text data to make it suitable for further analysis. This typically includes several key techniques aimed at breaking down text into manageable units and standardizing it for easier interpretation.

Tokenization is a fundamental preprocessing technique that involves splitting the text into individual tokens, such as words or phrases. This process enables the subsequent analysis to identify and manipulate these discrete elements effectively.

Normalizing text refers to standardizing the text to a consistent format. This might include transforming all text to lower case, correcting spelling variations, or even converting numbers to a standardized format. Normalization ensures that the data is uniform, which is crucial for accurate analysis.

Removing stop words is another essential preprocessing step. Stop words are common words, such as "and," "the," or "is," that do not carry significant meaning and can skew the results of text analysis. Eliminating these words allows the focus to be on more informative content within the clinical text.

Presenting extracted information using graphs is a post-processing step, rather than a part of preprocessing. Once the data has been effectively parsed, cleaned, and organized, visual representation becomes important for interpreting the results. This step focuses on communicating the insights derived from the analyzed data, which is