# Strengths and Limitations

This document provides an honest assessment of Nasty's capabilities, helping you understand where it excels, where it falls short, and how to leverage it effectively in real-world applications.

## Table of Contents

- [Core Philosophy](#core-philosophy)
- [What Nasty Does Best](#what-nasty-does-best)
- [Current Limitations](#current-limitations)
- [When to Use Nasty](#when-to-use-nasty)
- [When NOT to Use Nasty](#when-not-to-use-nasty)
- [Pretrained Models and Fine-Tuning](#pretrained-models-and-fine-tuning)
- [Integration with Ragex](#integration-with-ragex)
- [Practical Recommendations](#practical-recommendations)

## Core Philosophy

Nasty treats natural language with the same rigor as programming languages, building a complete grammatical Abstract Syntax Tree (AST) for every sentence. This "grammar-first" approach provides:

- **Structural Understanding**: Deep syntactic analysis beyond surface patterns
- **Explainability**: Every decision traces back to grammatical rules
- **Composability**: Transform ASTs using standard tree operations
- **Bidirectionality**: Convert natural language ↔ code with structural awareness

This is fundamentally different from statistical/neural-only approaches that learn patterns without explicit grammar representation.

## What Nasty Does Best

### 1. Grammatical Analysis and Structure

**Strengths:**
- Precise phrase structure parsing (NP, VP, PP)
- Accurate dependency extraction (Universal Dependencies)
- Clause detection (coordination, subordination)
- Complex sentence analysis
- Morphological feature extraction

**Use Cases:**
- Grammar checking and correction
- Language learning applications
- Syntactic pattern extraction
- Template-based text generation
- Controlled natural language interfaces

**Example:**
```elixir
# Parse complex sentence structure
text = "The professor who teaches mathematics works at the university."
{:ok, tokens} = English.tokenize(text)
{:ok, tagged} = English.tag_pos(tokens)
{:ok, doc} = English.parse(tagged)

# Extract precise syntactic dependencies
deps = English.DependencyExtractor.extract(doc)
# => Identifies subject, verb, relative clause, prepositional attachment
```

### 2. Multi-Language Support with Shared Architecture

**Strengths:**
- Language-agnostic AST schema
- Consistent API across English, Spanish, Catalan
- Morphological agreement handling
- Language-specific word order rules
- Bidirectional translation preserving structure

**Use Cases:**
- Multi-language content management
- Cross-lingual information extraction
- Translation with grammatical fidelity
- Language comparison and analysis

**Example:**
```elixir
# Parse in one language, render in another
{:ok, doc_en} = English.parse(tagged_en)
{:ok, doc_es} = Translator.translate_document(doc_en, :es)
{:ok, text_es} = Nasty.Rendering.Text.render(doc_es)
# Maintains grammatical structure with proper agreement
```

### 3. Code-Natural Language Interoperability

**Strengths:**
- Intent recognition from imperative sentences
- Natural language → Elixir code generation
- Code → Natural language explanation
- Constraint extraction from comparisons

**Use Cases:**
- Domain-specific language interfaces
- Query builders from natural language
- Code documentation generation
- Conversational programming interfaces

**Example:**
```elixir
# Generate code from natural language
{:ok, code} = Nasty.to_code(
  "Filter users where age greater than 21",
  source_language: :en,
  target_language: :elixir
)
# => "Enum.filter(users, fn u -> u.age > 21 end)"

# Explain code in natural language
{:ok, explanation} = Nasty.explain_code(
  "Enum.sort(numbers)",
  source_language: :elixir,
  target_language: :en
)
# => "Sort numbers"
```

### 4. Explainable NLP Pipeline

**Strengths:**
- Rule-based components provide transparency
- AST structure reveals decision path
- No "black box" transformations
- Debuggable at every stage

**Use Cases:**
- Regulated industries requiring explainability
- Educational tools showing linguistic analysis
- Research requiring interpretable results
- Debugging NLP pipelines

### 5. Pure Elixir Implementation

**Strengths:**
- No Python interop overhead
- Runs entirely in BEAM VM
- Leverage OTP supervision
- Easy deployment with Elixir apps
- No external API dependencies

**Use Cases:**
- Embedded in Elixir/Phoenix applications
- Serverless/edge deployments
- Offline processing
- Low-latency requirements

## Current Limitations

### 1. Lexical Coverage

**Limitations:**
- Limited vocabulary in translation lexicons (~300 words per language pair)
- No comprehensive dictionary lookup
- Unknown words pass through untranslated
- Domain-specific terminology requires manual addition

**Impact:**
- Poor translation quality for specialized text
- Incomplete entity recognition
- Missing lexical semantics

**Mitigation:**
- Expand lexicons incrementally for your domain
- Use fallback to original word
- Combine with external dictionary APIs
- Contribute domain-specific lexicons

### 2. Semantic Understanding

**Limitations:**
- No deep semantic analysis beyond SRL
- Limited world knowledge
- No common-sense reasoning
- Cannot resolve ambiguity requiring external knowledge
- Metaphors and idioms handled literally

**Impact:**
- Misses implied meanings
- Literal translations of idioms
- Cannot answer questions requiring inference
- Limited context understanding

**Mitigation:**
- Focus on factual, literal text
- Combine with knowledge bases
- Use for structure, not deep meaning
- Integrate pretrained models for semantics

### 3. Statistical Model Accuracy

**Limitations:**
- Rule-based POS tagging: ~85% accuracy
- HMM POS tagging: ~95% accuracy
- Neural POS tagging: 97-98% accuracy (but requires training)
- No pretrained models shipped by default
- Small training datasets limit performance

**Impact:**
- Errors compound through pipeline
- Complex sentences may parse incorrectly
- Needs domain-specific training for best results

**Mitigation:**
- Use neural models for critical applications
- Train on domain-specific data
- Implement error correction layers
- Ensemble multiple models

### 4. Parsing Incomplete Sentences

**Limitations:**
- Expects complete, well-formed sentences
- Fragments may fail to parse
- Informal text (tweets, chat) challenging
- Heavy reliance on punctuation
- Assumes standard grammar

**Impact:**
- Cannot process conversational text well
- Social media text needs preprocessing
- Bullet points and lists problematic

**Mitigation:**
- Preprocess text to add punctuation
- Use fallback parsing modes
- Detect fragments and handle separately
- Normalize text before parsing

### 5. Computational Cost

**Limitations:**
- Full parsing is CPU-intensive
- Neural models require significant memory
- Not optimized for real-time streaming
- Large documents can be slow

**Impact:**
- Latency for interactive applications
- Resource requirements for batch processing
- EXLA compilation overhead on first run

**Mitigation:**
- Cache parsed results
- Use rule-based models for speed
- Process in batches
- Profile and optimize hot paths

### 6. Rendering Quality

**Limitations:**
- Generated text can be unnatural
- May lose stylistic nuances
- Sentence simplification during parsing
- Limited paraphrasing capability

**Impact:**
- Roundtrip translation shows drift
- Generated summaries feel mechanical
- Cannot match human fluency

**Mitigation:**
- Use for technical/formal text
- Post-process generated text
- Combine with template systems
- Set user expectations appropriately

## When to Use Nasty

### Ideal Use Cases

1. **Structured Text Processing**
   - Technical documentation analysis
   - Legal/medical text with formal grammar
   - Controlled language systems
   - Template-based generation

2. **Multi-Language Applications**
   - Content management systems
   - Documentation translation
   - Language learning tools
   - Cross-lingual search

3. **Code-NL Integration**
   - DSL interfaces
   - Query builders
   - Documentation generation
   - Programming assistants

4. **Grammatical Analysis**
   - Grammar checking
   - Style analysis
   - Linguistic research
   - Educational applications

5. **Elixir-Native NLP**
   - Phoenix applications
   - Embedded NLP in Elixir apps
   - No Python interop needed
   - Offline processing

### Best Practices

- **Start Simple**: Begin with tokenization and POS tagging before full parsing
- **Validate Results**: Check parse quality on your specific domain
- **Train Models**: Fine-tune on domain-specific data for best accuracy
- **Combine Approaches**: Use rule-based for speed, neural for accuracy
- **Handle Errors**: Implement fallbacks for parsing failures
- **Expand Lexicons**: Add domain vocabulary incrementally
- **Cache Results**: Parse once, reuse AST multiple times

## When NOT to Use Nasty

### Poor Fit Scenarios

1. **Semantic-Heavy Tasks**
   - Open-domain question answering
   - Sentiment analysis (use dedicated models)
   - Topic modeling
   - Intent classification (without training)

2. **Informal Text**
   - Social media analysis
   - Chat/messaging data
   - Fragmented text
   - Heavy slang/abbreviations

3. **Real-Time Streaming**
   - Low-latency chat bots
   - Real-time speech processing
   - High-throughput pipelines
   - Sub-millisecond requirements

4. **Domain Without Training Data**
   - Highly specialized jargon
   - Mixed language text
   - Code-switched text
   - Non-standard dialects

### Better Alternatives

- **For Semantic Tasks**: Use transformer models (BERT, RoBERTa) via Bumblebee
- **For Sentiment**: Hugging Face models or cloud APIs
- **For Summarization**: Neural abstractive models
- **For Translation**: Neural MT systems (DeepL, Google Translate)
- **For Casual Text**: spaCy, NLTK with robust tokenization

## Pretrained Models and Fine-Tuning

### Current State

Nasty provides infrastructure for neural models but does NOT ship with pretrained models:

```elixir
# Neural POS tagging requires training
tagger = NeuralTagger.new(vocab_size: 10000, num_tags: 17)
{:ok, trained} = NeuralTagger.train(tagger, training_data, epochs: 10)
```

### Integration Opportunities

#### 1. Bumblebee for Embeddings and Transformers

**Strategy**: Use Bumblebee models for semantic tasks, Nasty for syntax

```elixir
# Bumblebee for embeddings
{:ok, model} = Bumblebee.load_model({:hf, "sentence-transformers/all-MiniLM-L6-v2"})
embeddings = Bumblebee.apply(model, text)

# Nasty for structure
{:ok, doc} = Nasty.parse(text, language: :en)
deps = English.DependencyExtractor.extract(doc)

# Combine: embeddings for similarity, AST for structure
```

**Benefits**:
- Leverage pretrained semantic understanding
- Keep grammatical precision from Nasty
- Best of both worlds

#### 2. Fine-Tune Neural POS Tagger

**Strategy**: Train Nasty's BiLSTM-CRF on domain data

```bash
# Download Universal Dependencies corpus
wget https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3226/ud-treebanks-v2.6.tgz

# Extract English corpus
tar -xzf ud-treebanks-v2.6.tgz
cd UD_English-EWT

# Train neural tagger
mix nasty.train.neural_pos \
  --corpus en_ewt-ud-train.conllu \
  --test en_ewt-ud-test.conllu \
  --output priv/models/en/pos_neural.axon \
  --epochs 10
```

**Expected Results**:
- 97-98% accuracy on standard benchmarks
- Domain adaptation with ~1000 annotated sentences
- 5-10x slower than rule-based

#### 3. Hybrid Architecture

**Recommendation**: Combine Nasty syntax with pretrained semantics

```elixir
defmodule HybridNLP do
  def analyze(text) do
    # Nasty for syntax
    {:ok, doc} = Nasty.parse(text, language: :en)
    
    # Extract sentences
    sentences = doc.paragraphs |> Enum.flat_map(& &1.sentences)
    
    # Bumblebee for semantic embeddings
    sentence_texts = Enum.map(sentences, &Nasty.Rendering.Text.render/1)
    embeddings = embed_with_bumblebee(sentence_texts)
    
    # Combine structural and semantic features
    %{
      syntax: doc,
      dependencies: English.DependencyExtractor.extract(doc),
      entities: English.EntityRecognizer.recognize(doc),
      embeddings: embeddings
    }
  end
end
```

## Integration with Ragex

[Ragex](https://github.com/am-kantox/ragex/) is a hybrid retrieval-augmented generation system for code analysis. Nasty and Ragex complement each other perfectly:

### Architecture Alignment

| Component | Ragex | Nasty |
|-----------|-------|-------|
| Input | Programming Language Code | Natural Language Text |
| AST | Code AST (Elixir/Erlang/Python) | Grammar AST (sentences/phrases) |
| Analysis | Functions, modules, calls | Entities, relations, structure |
| Search | Semantic + symbolic code search | Linguistic pattern matching |
| Output | Code understanding, refactoring | NL generation, translation |

### Complementary Strengths

1. **Code Documentation Pipeline**
   ```elixir
   # Ragex: Analyze code structure
   {:ok, analysis} = Ragex.analyze_file("lib/my_module.ex")
   
   # Nasty: Generate natural language documentation
   function_docs = Enum.map(analysis.functions, fn func ->
     # Convert function AST to natural language
     Nasty.explain_code(func.ast, source_language: :elixir, target_language: :en)
   end)
   ```

2. **Natural Language Code Search**
   ```elixir
   # User query in natural language
   query = "function that validates email addresses"
   
   # Nasty: Parse query to extract intent
   {:ok, intent} = Nasty.Interop.IntentRecognizer.recognize_from_text(query, language: :en)
   # => %Intent{action: "validate", target: "email addresses"}
   
   # Ragex: Semantic search in codebase
   {:ok, results} = Ragex.semantic_search(query)
   # => [%{node_id: "MyModule.validate_email/1", similarity: 0.85}]
   ```

3. **Bidirectional DSL**
   ```elixir
   # Natural language → Code (Nasty)
   {:ok, code} = Nasty.to_code("filter users by age", target_language: :elixir)
   
   # Code → Natural language (Nasty)
   {:ok, explanation} = Nasty.explain_code(code, target_language: :en)
   
   # Code analysis and refactoring (Ragex)
   {:ok, impact} = Ragex.find_function_impact("filter_users/1")
   ```

4. **Enhanced Code Understanding**
   ```elixir
   # Ragex: Extract code structure
   {:ok, graph} = Ragex.analyze_directory("lib/")
   
   # Nasty: Generate architectural summaries in natural language
   summary = graph.modules
   |> Enum.map(&describe_module/1)
   |> Enum.join("\n\n")
   # => "The MyApp.Users module manages user accounts. It depends on..."
   ```

### Concrete Integration Patterns

#### Pattern 1: Intelligent Code Comments

```elixir
defmodule CodeCommentGenerator do
  def generate_comment(function_ast) do
    # Ragex: Analyze function structure and dependencies
    {:ok, analysis} = Ragex.analyze_function(function_ast)
    
    # Extract: name, parameters, return type, calls
    template = build_template(analysis)
    
    # Nasty: Generate natural language from template
    {:ok, comment} = Nasty.render_template(template, language: :en)
    
    # => "@doc \"\"\"\n  Validates email addresses using regex pattern.\n  
    #     Returns {:ok, email} or {:error, reason}.\n    \"\"\""
  end
end
```

#### Pattern 2: Conversational Code Search

```elixir
defmodule ConversationalSearch do
  def search_codebase(user_query) do
    # Nasty: Parse natural language query
    {:ok, doc} = Nasty.parse(user_query, language: :en)
    {:ok, intent} = Nasty.Interop.IntentRecognizer.recognize(doc)
    
    # Extract search criteria from linguistic structure
    criteria = %{
      action: intent.action,        # "parse", "validate", "transform"
      target: intent.target,         # "JSON", "email", "user input"
      constraints: intent.constraints  # "where error handling exists"
    }
    
    # Ragex: Hybrid search with semantic + symbolic
    {:ok, results} = Ragex.hybrid_search(
      query: user_query,
      strategy: :fusion,
      graph_filter: build_filter(criteria)
    )
    
    # Nasty: Explain results in natural language
    explanations = Enum.map(results, fn result ->
      Nasty.explain_code(result.code, target_language: :en)
    end)
    
    {results, explanations}
  end
end
```

#### Pattern 3: Automated Documentation Generation

```elixir
defmodule AutoDocumentation do
  def document_module(module_path) do
    # Ragex: Extract module structure
    {:ok, analysis} = Ragex.analyze_file(module_path)
    
    doc_sections = [
      # Module overview
      overview: generate_overview(analysis.module),
      
      # Function descriptions (Nasty NL generation)
      functions: Enum.map(analysis.functions, fn func ->
        %{
          signature: func.name,
          description: explain_function(func),
          examples: generate_examples(func),
          see_also: find_related(func)  # via Ragex graph
        }
      end),
      
      # Dependencies (Ragex graph analysis)
      dependencies: Ragex.get_dependencies(analysis.module),
      
      # Usage patterns (combined)
      patterns: extract_usage_patterns(analysis)
    ]
    
    # Render as markdown
    render_markdown(doc_sections)
  end
end
```

### When to Use Both Together

**Use Ragex + Nasty when:**
- Building code documentation systems
- Creating conversational code search
- Generating technical writing from code
- Building DSLs with natural language interfaces
- Developing AI coding assistants
- Analyzing and explaining codebases

**Architecture Pattern:**
```
User Query (Natural Language)
       ↓
   [Nasty] Parse and understand intent
       ↓
   [Ragex] Search codebase with semantic + symbolic
       ↓
   [Nasty] Explain results in natural language
       ↓
User Response (Natural Language + Code)
```

## Practical Recommendations

### For Production Systems

1. **Start with Rule-Based Models**
   - Fastest performance
   - No training required
   - Good for well-formed text
   - Upgrade to neural when needed

2. **Implement Robust Error Handling**
   ```elixir
   case Nasty.parse(text, language: :en) do
     {:ok, doc} -> 
       process_document(doc)
     
     {:error, {:parse_incomplete, _}} ->
       # Fallback: simpler analysis
       {:ok, tokens} = English.tokenize(text)
       {:ok, tagged} = English.tag_pos(tokens)
       process_tokens(tagged)
     
     {:error, reason} ->
       Logger.warn("Parse failed: #{inspect(reason)}")
       {:error, :parse_failed}
   end
   ```

3. **Cache Parsed Results**
   ```elixir
   defmodule DocumentCache do
     use Agent
     
     def get_or_parse(text) do
       case Agent.get(__MODULE__, &Map.get(&1, cache_key(text))) do
         nil ->
           {:ok, doc} = Nasty.parse(text, language: :en)
           Agent.update(__MODULE__, &Map.put(&1, cache_key(text), doc))
           doc
         
         cached -> cached
       end
     end
   end
   ```

4. **Monitor Performance**
   - Track parse times
   - Measure accuracy on test set
   - A/B test rule vs. neural models
   - Profile memory usage

5. **Plan for Incremental Improvement**
   - Start with basic tokenization
   - Add POS tagging when needed
   - Full parsing for critical features
   - Neural models for accuracy

### For Development

1. **Use Examples as Learning Tools**
   - Run all examples to understand capabilities
   - Modify examples for your use case
   - Check `docs/` for detailed guides

2. **Inspect AST Structure**
   ```elixir
   {:ok, doc} = Nasty.parse(text, language: :en)
   IO.inspect(doc, limit: :infinity, pretty: true)
   ```

3. **Test on Your Domain**
   - Collect representative samples
   - Measure accuracy
   - Identify common failure modes
   - Extend lexicons accordingly

4. **Contribute Back**
   - Report bugs with examples
   - Submit lexicon additions
   - Share domain-specific improvements
   - Document your use cases

## Conclusion

Nasty excels at **grammatical analysis** and **structural manipulation** of natural language, making it ideal for applications requiring linguistic precision, multi-language support, and code interoperability. Its grammar-first approach provides explainability and composability that purely statistical systems lack.

However, Nasty is NOT a general-purpose NLP solution. It has limited lexical coverage, shallow semantic understanding, and requires careful domain adaptation. For best results:

1. **Use Nasty for structure, pretrained models for semantics**
2. **Combine with Ragex for code-related tasks**
3. **Train on domain-specific data**
4. **Implement robust fallbacks**
5. **Set realistic expectations**

The future of Nasty lies in hybrid architectures that combine its grammatical rigor with the semantic power of pretrained transformers, creating systems that understand both the form and meaning of natural language.