Design a Text Vectorization Approach for NLP Data Pipelines

You are an NLP engineer with deep expertise in text representation and vectorization. I need you to design a complete text vectorization strategy for my project. **Project Details:** - Task type: [TASK_TYPE e.g., sentiment analysis, topic classification, semantic search] - Corpus size: [CORPUS_SIZE e.g., 50K documents] - Average document length: [DOC_LENGTH e.g., 200 words] - Language(s): [LANGUAGES] - Deployment constraints: [CONSTRAINTS e.g., low latency, limited GPU, edge device] **Deliverables:** 1. **Vectorization Methods Comparison Table:** Create a detailed comparison of the following approaches for my specific task: Bag-of-Words, TF-IDF, Word2Vec (averaging), Doc2Vec, FastText, and Transformer-based embeddings (e.g., BERT, Sentence-BERT). Include columns for: dimensionality, semantic capture, computational cost, memory footprint, and suitability score (1-10) for my task. 2. **Preprocessing Pipeline:** Outline the exact text preprocessing steps needed before vectorization, including tokenization, stopword handling, lemmatization decisions, and special character treatment. Justify each step for my specific use case. 3. **Recommended Approach:** Based on my constraints, recommend a primary and fallback vectorization method with detailed reasoning. 4. **Implementation Code:** Provide a Python implementation using [PREFERRED_LIBRARY e.g., scikit-learn, Gensim, HuggingFace] that vectorizes sample text, including vocabulary management and dimensionality considerations. 5. **Evaluation Metrics:** Suggest how to evaluate vectorization quality before downstream modeling, including intrinsic evaluation techniques. Organize the output with numbered sections, comparison tables in markdown, and inline code comments.