Analytical MCP Server

# Advanced Fact Extraction Implementation Plan ## Overview This document outlines the plan for enhancing the Analytical MCP Server with advanced fact extraction techniques. The current implementation has basic fact extraction capabilities, but lacks sophisticated named entity recognition, coreference resolution, and relationship extraction capabilities. ## Current Limitations 1. **Named Entity Recognition**: - Only identifies persons via basic POS tagging - Empty placeholders for organizations and locations - No handling of dates, quantities, or other entity types 2. **Relationship Extraction**: - Basic pattern matching using simple POS tag patterns - Limited to pre-defined relationship patterns - No semantic understanding of relationships 3. **Coreference Resolution**: - No implementation of coreference resolution - Unable to connect pronouns to their antecedents - Cannot resolve entity references across sentences 4. **Fact Confidence Scoring**: - Simplistic confidence scoring mechanism - No contextual understanding for confidence assessment ## Implementation Strategy ### 1. Enhanced Named Entity Recognition #### Approach - Utilize Hugging Face inference API for state-of-the-art NER - Implement a fallback approach using natural.js and custom rules - Create a comprehensive entity type system #### Tasks 1. Create a new `AdvancedNER` class with: - Primary HuggingFace NER implementation - Fallback rule-based approach - Entity type classification system 2. Enhance entity recognition to include: - Persons (with titles and roles) - Organizations (with types) - Locations (with sub-types: cities, countries, etc.) - Dates and time expressions - Quantities and measurements - Products and services 3. Implement entity disambiguation 4. Add entity linking capabilities ### 2. Coreference Resolution #### Approach - Implement custom coreference resolution using mention-pair model - Integrate with existing NLP toolkit - Include pronoun resolution and nominal mentions #### Tasks 1. Create a new `CoreferenceResolver` class: - Mention detection system - Mention clustering algorithm - Chain formation logic 2. Implement specific resolution strategies for: - Pronoun resolution (he, she, it, they, etc.) - Nominal resolution (the company, the person, etc.) - Proper name resolution (shortened names, abbreviations) 3. Add coreference chain extraction for document-level understanding 4. Develop confidence scoring for coreference resolution ### 3. Advanced Relationship Extraction #### Approach - Implement dependency parsing-based relationship extraction - Support for binary, ternary, and nested relationships - Create a comprehensive relationship taxonomy #### Tasks 1. Create a new `RelationshipExtractor` class: - Dependency parsing integration - Relationship pattern detection - Semantic role labeling 2. Support extraction of: - Binary relationships (subject-verb-object) - Ternary relationships (subject-verb-object-complement) - Nested relationships (relationships within relationships) - Temporal relationships (with time context) 3. Implement a relationship classification system 4. Add confidence scoring for relationship extraction ### 4. Fact Integration and Enhancement #### Approach - Create a unified fact extraction system - Implement cross-validation between different extraction techniques - Develop a comprehensive confidence scoring system #### Tasks 1. Update `EnhancedFactExtractor` class to: - Integrate all extraction techniques - Resolve conflicts between extraction results - Merge related facts 2. Implement advanced confidence scoring: - Context-based confidence - Source reliability assessment - Cross-validation confidence boosting 3. Add fact clustering and summarization 4. Create a fact enrichment system with external knowledge ## Implementation Phases ### Phase 1: Infrastructure Enhancement (Week 1) 1. Setup Hugging Face inference integration 2. Create base classes for all components 3. Implement dependency parsing integration 4. Enhance existing NLP toolkit ### Phase 2: Core Implementation (Weeks 2-3) 1. Build Advanced NER system 2. Implement Coreference Resolution 3. Develop Relationship Extraction 4. Create unified fact extraction ### Phase 3: Integration and Testing (Week 4) 1. Integrate all components 2. Develop comprehensive test suite 3. Fine-tune performance and accuracy 4. Document the implementation ## Testing Strategy ### Unit Tests - Create tests for each extraction component - Test with challenging linguistic examples - Measure accuracy against ground truth ### Integration Tests - Test combined extraction pipeline - Verify cross-component interactions - Measure end-to-end performance ### Benchmark Tests - Compare against previous implementation - Measure performance metrics - Evaluate on standard NLP datasets ## Required Dependencies 1. **Primary Dependencies**: - @huggingface/inference (already installed) - natural (already installed) - wink-nlp (to be added) - compromise (to be added) 2. **Development Dependencies**: - Additional test libraries - Benchmark data ## Success Metrics 1. **Accuracy Metrics**: - NER F1 score > 0.85 - Coreference resolution F1 score > 0.75 - Relationship extraction F1 score > 0.70 2. **Performance Metrics**: - Processing time < 500ms for typical document - Memory usage < 200MB 3. **Quality Metrics**: - Fact confidence correlation with human judgment > 0.80 - False positive rate < 0.15 ## Conclusion This implementation plan provides a comprehensive strategy for enhancing the fact extraction capabilities of the Analytical MCP Server. The proposed approach combines state-of-the-art techniques with practical implementation considerations to deliver a robust and accurate fact extraction system.