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

If you are interested in more structured deep dives, check out my Blog or my Research Papers. For my work on tools and libraries, visit the Open Source section. You can also explore more daily notes in the TIL Index.