Text classification with vector embeddings — and no ML model

 Text classification using vector embeddings without an explicit ML model is possible through techniques like vector similarity search or rule-based classification. Here's an outline of how you can achieve this:


1. Define Categories

Decide on the categories you want to classify text into, e.g., "Sports," "Technology," "Politics," etc.

2. Generate Category Embeddings

Create representative embeddings for each category. These can be:

  • Predefined Keywords: Select key phrases for each category and compute their embeddings (e.g., "football" for Sports).
  • Example Texts: Use embeddings of prototypical texts for each category.

Use pre-trained models like Sentence-BERT, OpenAI’s embeddings, or similar tools to generate these.

3. Embed Input Text

Generate an embedding for the input text using the same embedding model as used for the category embeddings.

4. Measure Similarity

Compare the input text embedding with each category embedding. Common similarity metrics:

  • Cosine Similarity: Measures the cosine of the angle between two vectors.
  • Euclidean Distance: Measures the straight-line distance between two vectors.

5. Classify Text

Assign the text to the category with the highest similarity score. Optionally, set a threshold to handle cases where no category is a good match.

Advantages

  • No explicit training is required.
  • Adaptable to new categories by adding new representative embeddings.
  • Lightweight and interpretable.

Example Implementation in Python

Here’s a basic implementation using a pre-trained embedding model like sentence-transformers:

from sentence_transformers import SentenceTransformer, util
import numpy as np

# Load pre-trained embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Define category labels and example texts
categories = {
    "Sports": "football, basketball, and sports events",
    "Technology": "artificial intelligence, programming, and tech trends",
    "Politics": "elections, government policies, and diplomacy",
}

# Generate category embeddings
category_embeddings = {cat: model.encode(text, convert_to_tensor=True) for cat, text in categories.items()}

# Input text
input_text = "The latest advancements in AI are revolutionary."
input_embedding = model.encode(input_text, convert_to_tensor=True)

# Compute similarities
similarities = {cat: util.cos_sim(input_embedding, emb).item() for cat, emb in category_embeddings.items()}

# Assign category with highest similarity
predicted_category = max(similarities, key=similarities.get)
print(f"Input text: {input_text}")
print(f"Predicted category: {predicted_category}")

Applications

  • Document sorting
  • Topic tagging
  • Keyword-driven text analysis

Would you like further details or improvements on this method?