Enhance ATS Optimizer with Improved Keyword Extraction and Semantic Analysis #19
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This commit introduces several enhancements to the ATS optimizer script and configuration:
1. **Configuration Updates**:
- Added `semantic_validation: True` to enable semantic filtering of keywords by default.
- Increased `similarity_threshold` from 0.6 to 0.65 for stricter semantic categorization.
- Adjusted `ngram_range` and `whitelist_ngram_range` from `[1, 3]` to `[1, 2]` to focus on shorter, more precise phrases.
2. **Script Improvements**:
- **Entity Ruler Enhancement**: Added whitelisted phrases from `skills_whitelist` as `SKILL` entities in the spaCy pipeline, preserving multi-word skills during tokenization.
- **Keyword Extraction**:
- Updated `_process_doc_tokens` to prioritize `SKILL` entities, ensuring they are preserved as whole phrases before tokenizing remaining text.
- Improved `_generate_ngrams` to filter out single-letter tokens and stop words, enhancing TF-IDF accuracy.
- Refined `extract_keywords` to integrate `SKILL` entity extraction with regular tokenization, combining both for robust keyword lists.
- **TF-IDF Matrix**:
- Enhanced `_create_tfidf_matrix` to validate keyword sets, removing invalid tokens (e.g., single-letter words) before vectorization.
- Added debug logging for validated keyword sets.
- **Model Loading**: Added `sentencizer` to the spaCy pipeline in `_try_load_model` for consistent sentence boundary detection.
- **Minor Fixes**: Removed unused `Pool` import from multiprocessing, streamlined imports.
These changes improve keyword precision, preserve critical multi-word phrases, and enhance semantic analysis, making the optimizer more effective for ATS systems.
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This pull request enhances the ATS optimizer by refining keyword extraction, improving semantic analysis, and updating the configuration for better performance. Key changes include:
Config Updates:
semantic_validationby default and raisedsimilarity_thresholdto 0.65 for more accurate categorization.ngram_rangeandwhitelist_ngram_rangeto[1, 2]for focused, concise keyword extraction.Script Enhancements:
skills_whitelistinto the spaCy entity ruler asSKILLentities, ensuring multi-word skills (e.g., "machine learning") are preserved intact.extract_keywordsto combineSKILLentities with tokenized keywords, boosting accuracy._create_tfidf_matrixwith pre-vectorization validation and debugging for better traceability.sentencizerto spaCy pipeline for consistent sentence segmentation.Benefits:
Testing:
Reviewer Notes:
Closes #<issue_number> (if applicable).