Here's a tabular representation of different NLP use cases and the technologies used (ML, DL, etc.) for each:
| Use Case | Technology Used | Description |
|---|---|---|
| Text Classification | ML (Logistic Regression, SVM), DL (CNN, LSTM) | Categorizing text into predefined labels (e.g., spam detection, sentiment analysis). |
| Text Summarization | DL (Transformers, LSTMs, GRUs) | Generating concise versions of long text while preserving key information. |
| Machine Translation | DL (Seq2Seq models, Transformers) | Automatically translating text from one language to another. |
| Named Entity Recognition (NER) | ML (CRF, SVM), DL (Bi-LSTM, Transformers) | Identifying proper names, locations, organizations, etc., in text. |
| Part-of-Speech (POS) Tagging | ML (HMM, CRF), DL (Bi-LSTM, Transformers) | Assigning grammatical tags (e.g., noun, verb) to words in a sentence. |
| Clustering (Topic Modeling) | ML (LDA, K-Means), DL (Autoencoders) | Grouping similar documents based on themes without predefined categories. |
| Dependency Parsing | ML (Graph-based models), DL (Bi-LSTM, Transformers) | Analyzing grammatical structure by establishing relationships between words. |
| Constituency Parsing | ML (PCFG), DL (Transformers) | Breaking sentences into nested sub-phrases based on syntax rules. |
| Regular Expression Matching | Rule-Based, ML (HMM) | Finding patterns in text, such as extracting phone numbers or email addresses. |
| Finding Similar Words (Word Embeddings) | DL (Word2Vec, GloVe, FastText) | Mapping words to high-dimensional vectors based on contextual meaning. |
| Large Language Models (LLMs) & Prompt Engineering | DL (GPT, BERT, LLaMA) | Fine-tuning and using pre-trained models for various NLP tasks. |
| Building Foundational Models | DL (Transformers, Self-Supervised Learning) | Training massive models on large datasets for broad NLP capabilities. |