NLP Data Processing Pipelines: From Raw Text to Model Input

Advanced

🔄 NLP Data Processing Pipeline

Effective NLP models rely on robust data processing pipelines. A typical NLP pipeline includes the following steps:

📥 1. Text Collection

Gathering data from sources like social media, documents, or speech transcripts.

🧹 2. Text Cleaning

Removing noise such as HTML tags, special characters, and correcting typos.

✂️ 3. Tokenization

Breaking text into tokens (words, subwords, or sentences).

🔧 4. Normalization

Standardizing tokens through:

  • 🔠 Lowercasing
  • 🌱 Stemming
  • 🧾 Lemmatization

🚫 5. Stopword Removal

Eliminating common words (e.g., “the”, “and”) that carry little semantic value.

🔢 6. Vectorization

Converting text into numerical form using techniques like:

  • 📊 TF-IDF
  • 🧠 Word Embeddings

💻 Example: Tokenization and Vectorization in Python

from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["Natural language processing is fascinating.", "NLProc enables machines to understand language."]
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)
print(vectorizer.get_feature_names_out())
print(X.toarray())

🧠 Final Insight

This pipeline prepares raw text for machine learning models, ensuring they can understand and learn from the data effectively.

🧩 Diagram: NLP Preprocessing Pipeline

Text Collection
       |
       v
 Text Cleaning
       |
       v
 Tokenization
       |
       v
 Normalization
       |
       v
Stopword Removal
       |
       v
 Vectorization
       |
       v
ML-Ready Features