Step 1: Tokenization

Breaking text into smaller units called tokens, usually words or subwords. The sentence "I love NLP!" becomes ["I", "love", "NLP", "!"]. This seems simple but gets tricky with contractions, hyphenated words, and compound phrases.

Step 2: Text Normalization

Cleaning and standardizing text through several processes:

  •  Lowercasing: Converting "Apple" and "apple" to the same form
  •  Removing Punctuation: Cleaning out special characters when they don't add meaning
  •  Removing Stop Words: Filtering out common words like "the," "is," "at" that don't carry much meaning
  •  Stemming/Lemmatization: Reducing words to their root form ("running," "runs," "ran" all become "run")

Step 3: Feature Extraction

Converting text into numbers that algorithms can process. Common approaches include:

  •  Bag of Words: Counting word occurrences, ignoring order. "The cat sat on the mat" becomes a count of each word.
  •  TF-IDF: Weighing words by importance - common words get lower scores, rare but meaningful words get higher scores.
  •  Word Embeddings: Representing words as dense vectors that capture semantic relationships. Words with similar meanings have similar vectors.
  •  Important Concept: Word embeddings like Word2Vec or GloVe learn that "king" - "man" + "woman" ≈ "queen" by analyzing how words appear in context across millions of sentences.

Step 4: Model Application

Applying machine learning models to perform the desired task. This could be a simple classifier, a recurrent neural network, or a sophisticated transformer model depending on the complexity of the task.