Natural Language Processing (NLP): Text Preprocessing, Feature Extraction, and Word Embeddings…

2026-06-18 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article provides a foundational overview of Natural Language Processing (NLP) concepts, detailing essential terminology and core techniques. It begins by defining terms like corpus, document, vocabulary, and tokens, illustrating the basic NLP data flow. The content then elaborates on text preprocessing methods, including tokenization, stemming (e.g., PorterStemmer), lemmatization (e.g., WordNetLemmatizer), and stopword removal, highlighting the differences between stemming and lemmatization. It also covers Parts of Speech (POS) tagging, explaining common tags and their application. Subsequently, the article describes feature extraction techniques such as One-Hot Encoding, Bag of Words (BoW) in binary and count forms, N-Grams, and TF-IDF, outlining their advantages and disadvantages. Finally, it introduces word embeddings, specifically Word2Vec (CBOW and Skip-Gram architectures), and Average Word2Vec, emphasizing their ability to capture semantic relationships and reduce dimensionality compared to sparse representations.

Key takeaway

For machine learning engineers building NLP models, understanding the nuances of text preprocessing and feature extraction is crucial. You should carefully select between stemming and lemmatization based on the need for semantic preservation, and choose vectorization methods like TF-IDF or Word2Vec depending on whether word importance or semantic relationships are paramount for your specific task. This foundational knowledge directly impacts model performance and interpretability.

Key insights

NLP transforms raw text into numerical representations through preprocessing and feature extraction to enable machine understanding.

Principles

• Lemmatization yields valid root words, preserving meaning better than stemming.
• Word embeddings capture semantic relationships, unlike sparse encoding methods.
• Increasing N in N-Grams captures more contextual information.

Method

The NLP text processing flow involves converting a corpus into documents, then tokens, and finally a unique vocabulary for analysis. TF-IDF calculates word importance by multiplying term frequency and inverse document frequency.

In practice

• Use NLTK's "WordNetLemmatizer" for accurate root word extraction.
• Apply Word2Vec for tasks requiring semantic understanding like sentiment analysis.
• Employ Average Word2Vec to represent entire sentences as fixed-length vectors.

Topics

• Natural Language Processing
• Text Preprocessing
• Word Embeddings
• Feature Extraction
• Tokenization
• Word2Vec
• TF-IDF

Best for: AI Student, Machine Learning Engineer, NLP Engineer

Related on AIssential

• The Ultimate NLP Roadmaps; From text Preprocessing to Word Embeddings. — A structured NLP roadmap progresses from text cleaning and vectorization to advanced neural network-based word embeddings.
• Natural language processing made easy — NLP simplifies human language for computer analysis through tokenization, embeddings, and statistical modeling.
• From Raw Text to Machine Intelligence: A Complete NLP Pipeline Guide — NLP pipelines convert raw human language into structured numerical representations for machine learning models through sequential processing.
• Text Preprocessing in NLP: Cleaning Text Before Machines Can Understand It — Text preprocessing cleans raw language data for machine learning models to improve accuracy and reduce computational load.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.