Natural Language Processing Capabilities¶

Overview¶

AIDDDMAP's NLP system provides advanced natural language processing capabilities for understanding, generating, and analyzing text data. The system supports multiple languages, custom model training, and integration with various data sources.

Core Features¶

1. Text Understanding¶

Named Entity Recognition (NER)
Part-of-Speech (POS) Tagging
Dependency Parsing
Semantic Role Labeling

2. Text Generation Configuration¶

Language Model Integration
Template-based Generation
Controlled Text Generation
Multi-lingual Support

3. Text Analysis¶

Sentiment Analysis
Topic Modeling
Text Classification
Relationship Extraction

4. Conversation Processing¶

Intent Recognition
Entity Extraction
Dialog Management
Context Tracking

Implementation¶

1. NLP Configuration¶

interface NLPConfig {
  language: string[];
  models: ModelConfig[];
  pipeline: PipelineConfig;
  output: OutputConfig;
}

interface ModelConfig {
  type: ModelType;
  path: string;
  parameters: ModelParameters;
  optimization?: OptimizationConfig;
}

enum ModelType {
  TRANSFORMER = "transformer",
  BERT = "bert",
  GPT = "gpt",
  CUSTOM = "custom",
}

2. Text Handling¶

interface TextProcessor {
  preprocessing: PreprocessingConfig;
  tokenization: TokenizationConfig;
  analysis: AnalysisConfig;
}

interface PreprocessingConfig {
  cleaning: boolean;
  normalization: boolean;
  language: string;
  custom?: CustomPreprocessor[];
}

interface TokenizationConfig {
  type: "wordpiece" | "bpe" | "sentencepiece";
  vocabulary: string;
  maxLength: number;
}

3. Model Pipeline¶

interface NLPPipeline {
  stages: PipelineStage[];
  configuration: PipelineConfig;
  optimization: OptimizationConfig;
}

interface PipelineStage {
  name: string;
  type: StageType;
  model?: ModelConfig;
  config: StageConfig;
}

Language Understanding¶

1. Entity Recognition¶

interface NERConfig {
  model: string;
  entities: string[];
  confidence: number;
  context: boolean;
}

interface NERResult {
  entities: Entity[];
  text: string;
  confidence: number;
}

interface Entity {
  text: string;
  type: string;
  start: number;
  end: number;
  confidence: number;
  metadata?: any;
}

2. Semantic Analysis¶

interface SemanticConfig {
  type: "classification" | "similarity" | "embedding";
  model: string;
  labels?: string[];
  threshold?: number;
}

interface SemanticResult {
  label?: string;
  score: number;
  embedding?: number[];
  confidence: number;
}

Text Generation¶

1. Generation Configuration¶

interface GenerationConfig {
  model: string;
  parameters: {
    maxLength: number;
    temperature: number;
    topK: number;
    topP: number;
    repetitionPenalty: number;
  };
  constraints?: GenerationConstraints;
}

interface GenerationConstraints {
  topics?: string[];
  style?: string;
  format?: string;
  prohibited?: string[];
}

2. Template System¶

interface TemplateConfig {
  templates: Template[];
  variables: Variable[];
  constraints: TemplateConstraints;
}

interface Template {
  id: string;
  text: string;
  slots: Slot[];
  metadata: TemplateMetadata;
}

Integration Examples¶

1. Text Analysis Pipeline¶

// Configure text analysis pipeline
const analyzer = new TextAnalyzer({
  pipeline: [
    {
      type: "preprocessing",
      config: {
        cleaning: true,
        normalization: true,
        language: "en",
      },
    },
    {
      type: "ner",
      model: "spacy",
      config: {
        entities: ["PERSON", "ORG", "GPE"],
        confidence: 0.5,
      },
    },
    {
      type: "classification",
      model: "bert-base",
      config: {
        labels: ["positive", "negative", "neutral"],
        threshold: 0.7,
      },
    },
  ],
  output: {
    format: "json",
    include: ["entities", "sentiment", "metadata"],
  },
});

// Process text
const analysis = await analyzer.analyze({
  text: inputText,
  requirements: {
    ner: true,
    classification: true,
  },
});

2. Text Generation¶

// Configure text generator
const generator = new TextGenerator({
  model: {
    type: "gpt",
    path: "gpt-neo-1.3B",
    parameters: {
      maxLength: 100,
      temperature: 0.7,
      topP: 0.9,
    },
  },
  constraints: {
    topics: ["technology", "science"],
    style: "professional",
    format: "paragraph",
  },
  output: {
    format: "markdown",
    metadata: true,
  },
});

// Generate text
const generated = await generator.generate({
  prompt: "Explain artificial intelligence",
  parameters: {
    length: 200,
    style: "educational",
  },
});

3. Conversation Processing¶

// Configure conversation handler
const conversationHandler = new ConversationHandler({
  intent: {
    model: "bert",
    confidence: 0.7,
    fallback: "default",
  },
  entity: {
    model: "spacy",
    types: ["product", "service", "location"],
  },
  context: {
    tracking: true,
    window: 5,
    storage: "memory",
  },
});

// Process conversation
const response = await conversationHandler.process({
  text: userInput,
  sessionId: "user123",
  context: previousContext,
});

Performance Optimization¶

1. Model Optimization¶

interface ModelOptimization {
  quantization?: {
    type: "dynamic" | "static";
    precision: "int8" | "float16";
  };
  pruning?: {
    method: "magnitude" | "structured";
    ratio: number;
  };
  distillation?: {
    teacher: string;
    temperature: number;
  };
}

2. Runtime Optimization¶

interface RuntimeConfig {
  batchSize: number;
  caching: CacheConfig;
  threading: ThreadConfig;
  memory: MemoryConfig;
}

Best Practices¶

1. Model Selection¶

Choose appropriate model size
Consider language requirements
Balance accuracy and speed
Regular model updates

2. Text Processing¶

Proper preprocessing
Handle multiple languages
Implement error handling
Maintain context

3. Deployment¶

Model optimization
Resource management
Monitoring
Version control

Future Enhancements¶

Planned Features
Zero-shot learning
Cross-lingual transfer
Improved context handling
Advanced generation control
Research Areas
Few-shot learning
Model compression
Multilingual models
Ethical AI