Arabic NLP Fundamentals: From Sparse Embeddings and BM25 to Dynamic Programming

Rebuilding NLP fundamentals for Arabic: TF-IDF, BM25, sparse embeddings, minimum edit distance, and learning problem-solving through LeetCode and Jurafsky’s Speech and Language Processing book.

Fixing the Gap: Classical NLP Fundamentals

For years, I’ve skimmed over foundational NLP concepts:

• TF-IDF and BM25 (lexical retrieval)
• Sparse embeddings (n-gram subsampling + MLP compression)
• Dynamic programming algorithms (edit distance, Viterbi)

I understood the new stuff—BERT, transformers, semantic embeddings—but the classical foundations felt disconnected. The gap became obvious: I never built these algorithms myself. I never saw why sparse embeddings exist (they’re solving speed + interpretability problems dense methods don’t) or how to optimize them.

I decided to rebuild from first principles using:

1. Jurafsky & Martin’s “Speech and Language Processing” (3rd edition draft)
2. Implementing algorithms in Arabic code
3. Problem-solving practice through LeetCode

Learning Roadmap

Phase 1: Lexical Methods

• Word2vec and n-gram theory
• Stemming and tokenization for Arabic
• TF-IDF and BM25 ranking
• BMX (dense-sparse hybrid from Mixedbread)

Phase 2: Sparse Embeddings

• SPLADE and learned sparse vectors
• Training sparse embeddings with SBERT
• Optimization and interpretability

Phase 3: Dynamic Programming & Alignment (current)

• Minimum edit distance (Levenshtein)
• Word alignment algorithms
• Viterbi and HMMs

Each builds on the last. You can’t understand why sparse embeddings matter without understanding BM25. You can’t implement them efficiently without mastering dynamic programming.

First Chapter: Tokenization, Unicode & Minimum Edit Distance

Chapter 1 of Jurafsky covers:

• Words and tokens: What counts as a “word” when you’re dealing with Arabic morphology?
• Unicode and encoding: How different languages store characters differently (critical for Arabic preprocessing)
• Regular expressions: Practical NLP pattern matching
• Minimum edit distance: The algorithm that powers spell-checking and text similarity

The minimum edit distance is brilliant but requires implementation from scratch. You need to think about:

• State representation (the matrix)
• Recurrence relations (how cells depend on neighbors)
• Backtracking (to recover the actual edits)

I couldn’t implement it cleanly at first. That’s when I realized: my problem-solving muscles are rusty.

Deliberate Practice: NeetCode + ML Problems

I found NeetCode—cleaner and more focused than LeetCode. In the last week:

• Solved 11 problems (3 easy, 8 medium+)
• Built Python fluency around collections.Counter, hashmaps, list comprehensions
• Code works but isn’t always optimal—that’s fine; repetition matters more than perfection at this stage
• The real win: restored my patience for thinking through problems step-by-step

The plan:

• Month 1: Finish 150 NeetCode problems, focus on dynamic programming (edit distance, Viterbi, longest common subsequence)
• Month 2: Transition to Deep-ML—same structure but for ML algorithms instead of data structures
• Parallel: Implement each NLP chapter concept in Arabic-ready code

Building: Smart Arabic Keyword Checker

The alignment chapter inspired a concrete project: build a spell checker for Arabic that understands context and domain.

Current keyboards (suggest random near-misses). A smarter version would:

1. Technical terminology mapping
   • Suggest Arabic equivalents for ML terms
   • Example: “embedding model” → “نماذج التضمين”
   • Example: “vector database” → “قاعدة بيانات المتجهات”
2. Persona-aware suggestions
   • Different recommendations for engineers, doctors, writers
   • Your vocabulary profile informs what “correct” means
3. Federated learning (like Gboard)
   • Learn from your typing without uploading data
4. Arabic grammar correction
   • Not just spelling; handle diacritics, agreement, tense
5. Adaptive learning
   • Improve suggestions based on what you accept/reject

The tech stack: dynamic programming for edit distance, sparse embeddings for semantic similarity, possibly Rust for performance.

References

1. The NLP book

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Internal Resources

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