Text Preprocessing
- (The University of Chicago - Alvin Wei-Cheng Wong)
- Overview
Text preprocessing involves cleaning and preparing raw text data for further analysis or model training. Proper text preprocessing can significantly impact the performance and accuracy of NLP models.
In NLP, text preprocessing is the initial step of cleaning and transforming raw text data into a structured format by performing operations like tokenization, removing stop words, stemming, or lemmatization, which allows the NLP model to analyze the text more effectively; essentially, it prepares the text for further processing by removing unnecessary elements and standardizing the format.
Common techniques:
Why is text preprocessing important?
- Tokenization: Breaking down text into individual words or units called "tokens".
- Lowercasing: Converting all text to lowercase.
- Stop word removal: Removing common words like "the," "a," and "is" that don't contribute significant meaning.
- Stemming: Reducing words to their root form (e.g., "walking" becomes "walk").
- Lemmatization: Finding the base form of a word based on its dictionary definition (considered more accurate than stemming).
Why is text preprocessing important?
- Improves accuracy: By removing irrelevant information, the NLP model can focus on the most important aspects of the text.
- Reduces computational complexity: Removing unnecessary data points can make processing faster.
- Standardizes data: Ensures that words are treated consistently regardless of their case or grammatical form.
[More to come ...]
