Text Preprocessing
Before most natural language processing tasks, it's necessary to clean up the text data using text preprocessing techniques.
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Key Concepts
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Text Preprocessing
Noise Removal
Tokenization
Text Normalization
Stemming
Lemmatization
Stopword Removal
Part-of-Speech Tagging
Text Preprocessing
Text Preprocessing
In natural language processing, text preprocessing is the practice of cleaning and preparing text data. NLTK and re are common Python libraries used to handle many text preprocessing tasks.
Text Preprocessing
Lesson 1 of 1
- 1Text preprocessing is an approach for cleaning and preparing text data for use in a specific context. Developers use it in almost all natural language processing (NLP) pipelines, including voice re…
- 2Text cleaning is a technique that developers use in a variety of domains. Depending on the goal of your project and where you get your data from, you may want to remove unwanted information, such a…
- 3For many natural language processing tasks, we need access to each word in a string. To access each word, we first have to break the text into smaller components. The method for breaking text into …
- 4Tokenization and noise removal are staples of almost all text pre-processing pipelines. However, some data may require further processing through text normalization. Text normalization is a catch…
- 5Stopwords are words that we remove during preprocessing when we don’t care about sentence structure. They are usually the most common words in a language and don’t provide any information about the…
- 7Lemmatization is a method for casting words to their root forms. This is a more involved process than stemming, because it requires the method to know the part-of-speech for each word. Since lemm…
- 8To improve the performance of lemmatization, we need to find the part of speech for each word in our string. In script.py, to the right, we created a part-of-speech tagging function. The functi…
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