Text Classification vs Token Classification
Text Classification and Token Classification are both essential techniques in Natural Language Processing (NLP). While Text Classification assigns entire documents or sentences into predefined categories, Token Classification labels individual words or subwords within a text. Understanding the differences between these approaches is crucial for selecting the right method for various NLP applications.
Overview of Text Classification
Text Classification involves categorizing entire pieces of text, such as documents, sentences, or paragraphs, into predefined labels.
Key Features:
- Classifies entire texts into categories (e.g., spam vs. not spam, topic classification)
- Uses supervised learning, deep learning, and traditional NLP methods
- Common models: Naïve Bayes, Support Vector Machines (SVM), LSTMs, and Transformers
Pros:
✅ Effective for large-scale text categorization ✅ Works well for sentiment analysis, spam detection, and topic modeling ✅ Requires less granular labeling compared to token classification
Cons:
❌ Does not provide word-level insights ❌ May struggle with complex multi-label texts ❌ Requires labeled training data for accurate classification
Overview of Token Classification
Token Classification assigns labels to individual words or subwords within a sentence.
Key Features:
- Works at the token level rather than the sentence or document level
- Used for tasks like Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and chunking
- Common models: Conditional Random Fields (CRF), BiLSTMs, Transformers like BERT
Pros:
✅ Provides fine-grained insights at the word level ✅ Essential for applications like NER, POS tagging, and syntax parsing ✅ Useful in extracting structured information from unstructured text
Cons:
❌ Requires more detailed annotation effort ❌ Can be computationally expensive for large datasets ❌ Context-dependent labeling may lead to errors
Key Differences
| Feature | Text Classification | Token Classification |
|---|---|---|
| Focus | Categorizing entire texts | Labeling individual words or tokens |
| Techniques Used | Machine learning, deep learning | CRF, BiLSTM, Transformers |
| Use Case | Spam detection, sentiment analysis, topic classification | Named Entity Recognition, POS tagging, chunking |
| Granularity | Document/Sentence level | Word/Subword level |
| Complexity | Lower | Higher (requires detailed labeling) |
When to Use Each Approach
- Use Text Classification when you need to assign a label to an entire document or sentence, such as for spam detection, sentiment analysis, or news categorization.
- Use Token Classification when you need word-level annotations, such as in Named Entity Recognition (NER), Part-of-Speech (POS) tagging, or extracting structured data from text.
Conclusion
Text Classification and Token Classification serve different purposes in NLP. While Text Classification assigns categories to whole texts, Token Classification provides detailed annotations at the word level. The choice depends on the level of granularity required for a given task. 🚀