A beginner’s guide to modern natural language processing

We built a clickbait classifier with three different NLP models. The simplest method surprisingly beat the more complex neural network. Here’s why.

#1about 5 minutes

Understanding the core challenge of natural language processing

Machine learning models require numerical inputs, so raw text must be converted into a numerical format called a vector or text embedding.

#2about 6 minutes

Exploring bag-of-words methods for text vectorization

Binary and count vectorization create features based on the presence or frequency of words in a document, ignoring their original context.

#3about 4 minutes

How Word2Vec captures word meaning in vector space

The Word2Vec model learns numerical representations for words by analyzing their surrounding context, grouping similar words closer together in a multi-dimensional space.

#4about 5 minutes

Training a Word2Vec model in Python using Gensim

A practical demonstration shows how to clean text data and train a custom Word2Vec model to generate embeddings for a specific vocabulary.

#5about 3 minutes

Creating document embeddings by averaging word vectors

A simple yet effective method to represent an entire document is to retrieve the embedding for each word and calculate their average vector.

#6about 2 minutes

Evaluating the performance of the Word2Vec classifier

The classifier trained on averaged word embeddings achieves 95% accuracy, with errors often occurring on headlines with misleading topics or tones.

#7about 3 minutes

Overcoming context limitations with transformer models

Transformer models use a self-attention mechanism to weigh the importance of other words in a sentence, allowing them to understand a word's meaning in its specific context.

#8about 5 minutes

Understanding how the BERT model is pre-trained

BERT learns a deep understanding of language by being pre-trained on tasks like predicting masked words and determining correct sentence order, enabling it to be fine-tuned for specific applications.

#9about 7 minutes

Fine-tuning a BERT model with the Transformers library

Using the Hugging Face Transformers library, a pre-trained DistilBERT model is fine-tuned for the clickbait classification task, requiring specific tokenization with attention masks.

#10about 2 minutes

Choosing the right text processing model for your task

While the fine-tuned BERT model achieves the highest accuracy at 99%, simpler methods like count vectorization can outperform Word2Vec and may be sufficient depending on the use case.

#11about 2 minutes

Using word embeddings to improve downstream NLP tasks

Word embeddings can be combined with other techniques, such as TF-IDF weighting, to extract more signal and improve performance on tasks like sentiment analysis.

#12about 2 minutes

Addressing overfitting and feature leakage in production

Preventing overfitting involves using validation sets, ensuring representative data samples, and checking for feature leakage where a feature inadvertently reveals the outcome.

#13about 2 minutes

Handling out-of-vocabulary and rare terms in NLP

For rare or out-of-vocabulary terms that models struggle with, symbolic rule-based approaches can be used as a complementary system to handle important edge cases.

#14about 3 minutes

Advice for starting a career in data science

Aspiring data scientists should focus on gaining hands-on experience with real-world datasets and building a portfolio of projects to develop an intuition for common issues.