Lexical Diversity Calculator
Measure vocabulary richness and linguistic complexity using multiple lexical diversity metrics including TTR, MTLD, HD-D, and more
Text Input & Settings
Enter your text and configure analysis settings. For reliable results, use at least 100 words.
Analysis Settings
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Ready to Analyze
Paste your text above and configure settings to calculate lexical diversity metrics including TTR, MTLD, MATTR, and more. Use at least 100 words for reliable results.
📊 Multiple Metrics
Calculate various lexical diversity measures including Type-Token Ratio (TTR), Moving-Average Type-Token Ratio (MATTR), Measure of Textual Lexical Diversity (MTLD), and Hypergeometric Distribution Diversity (HD-D) for comprehensive vocabulary analysis.
🎯 Research Applications
Essential for linguistic research, language learning assessment, authorship analysis, and educational evaluation. Compare texts across different genres, authors, or developmental stages to understand vocabulary sophistication.
Understanding Lexical Diversity
Lexical diversity, also known as vocabulary richness or lexical variation, measures how varied and sophisticated the vocabulary of a text is. It's a key indicator of linguistic complexity, writing quality, and language proficiency. Higher lexical diversity typically indicates more sophisticated language use, while lower diversity might suggest repetitive vocabulary or simplified language.
Why Lexical Diversity Matters
- Language Learning: Assessing vocabulary development and proficiency levels
- Educational Assessment: Evaluating writing quality and linguistic growth
- Linguistic Research: Comparing language use across genres, registers, and populations
- Authorship Analysis: Identifying stylistic patterns and author characteristics
- Clinical Applications: Detecting language disorders and cognitive changes
- Content Analysis: Measuring text complexity for different audiences
- Translation Studies: Comparing source and target text complexity
Factors Affecting Lexical Diversity
Text-Related Factors:
- • Text length and sample size
- • Genre and register (academic vs. casual)
- • Topic complexity and specificity
- • Text structure and organization
- • Purpose and intended audience
Author-Related Factors:
- • Language proficiency level
- • Educational background
- • Age and cognitive development
- • Cultural and linguistic background
- • Writing experience and expertise
Lexical Diversity Metrics Explained
Type-Token Ratio (TTR)
The most basic measure of lexical diversity, TTR is calculated as the number of unique words (types) divided by the total number of words (tokens). Values range from 0 to 1, with higher values indicating greater diversity.
TTR = Number of Unique Words / Total Number of Words
Advantages: Simple, intuitive, widely used
Limitations: Highly sensitive to text length; decreases as text gets longer
Best for: Comparing texts of similar length
Root TTR (RTTR)
An adjustment to TTR that partially corrects for text length effects by dividing the number of types by the square root of tokens. This provides a more stable measure across different text lengths.
RTTR = Number of Unique Words / √(Total Number of Words)
Advantages: Less sensitive to text length than TTR
Limitations: Still affected by text length, though to a lesser degree
Best for: Comparing texts of moderate length differences
Moving-Average Type-Token Ratio (MATTR)
MATTR calculates TTR for consecutive segments of fixed length throughout the text and then averages these values. This approach provides a more robust measure that is less affected by text length while capturing local lexical diversity patterns.
MATTR = Average of TTR values calculated for sliding windows of fixed size
Advantages: Robust to text length, captures local patterns
Limitations: Requires minimum text length for reliable calculation
Best for: Analyzing longer texts with consistent patterns
Measure of Textual Lexical Diversity (MTLD)
MTLD measures the average length of sequential word strings that maintain a TTR of 0.72. It represents the average number of words needed before vocabulary starts repeating significantly, providing a length-independent measure.
MTLD = Average length of word sequences with TTR ≥ 0.72
Advantages: Independent of text length, theoretically grounded
Limitations: Complex calculation, requires substantial text
Best for: Comparing texts of very different lengths
Hypergeometric Distribution Diversity (HD-D)
HD-D uses hypergeometric distribution to calculate the probability of encountering new vocabulary in a random sample of the text. It provides a probabilistic measure of lexical diversity that accounts for word frequency distributions.
HD-D = Expected number of types in random samples using hypergeometric distribution
Advantages: Statistically principled, accounts for frequency distribution
Limitations: Computationally intensive, complex interpretation
Best for: Detailed statistical analysis of vocabulary distribution
Interpretation Guidelines
TTR Interpretation
MTLD Interpretation
Contextual Considerations
Text Genre
- • Academic: Higher diversity expected
- • Conversation: Lower diversity normal
- • Fiction: Moderate to high diversity
- • Technical: Variable, domain-dependent
Language Level
- • Native speakers: Higher diversity
- • L2 learners: Lower diversity
- • Children: Age-dependent increase
- • Advanced users: Near-native levels
Text Length
- • Short texts: TTR may be inflated
- • Long texts: TTR decreases naturally
- • Use MTLD/MATTR for length stability
- • Compare similar-length texts
How to Use the Calculator
📝 Step 1: Input Your Text
Paste or type your text into the input area. For reliable results, use at least 100 words. The calculator accepts various text types including essays, articles, conversations, and literary works. Text preprocessing options help ensure accurate analysis.
⚙️ Step 2: Configure Settings
Choose whether to include function words (articles, prepositions) or focus only on content words. Select case sensitivity options and decide on handling of punctuation and numbers. These settings significantly impact diversity calculations.
📊 Step 3: Analyze Results
Review multiple diversity metrics to get a comprehensive picture. Compare TTR for quick assessment, use MTLD for length-independent comparison, and examine MATTR for detailed analysis. Consider your text type and purpose when interpreting scores.
🔍 Step 4: Compare and Improve
Use the word frequency analysis to identify repetitive vocabulary. The most frequent words list helps pinpoint areas for improvement. Export results for further analysis or comparison with other texts or benchmarks.
Applications and Use Cases
🎓 Educational Assessment
- • Writing Evaluation: Assess student writing development over time
- • Language Proficiency: Measure L2 learner vocabulary growth
- • Curriculum Planning: Design vocabulary-focused lessons
- • Placement Testing: Determine appropriate language levels
- • Progress Monitoring: Track lexical development
🔬 Research Applications
- • Corpus Linguistics: Compare language varieties and registers
- • Psycholinguistics: Study cognitive processing and memory
- • Sociolinguistics: Analyze language variation across groups
- • Computational Linguistics: Feature extraction for NLP
- • Historical Linguistics: Track language change over time
🩺 Clinical Applications
- • Language Disorders: Detect and monitor language impairments
- • Cognitive Assessment: Measure cognitive decline or recovery
- • Therapy Evaluation: Track treatment effectiveness
- • Diagnostic Tools: Support clinical decision-making
- • Developmental Monitoring: Assess language development
📚 Content Analysis
- • Authorship Attribution: Identify writing style patterns
- • Genre Classification: Distinguish text types automatically
- • Quality Assessment: Evaluate content sophistication
- • Readability Analysis: Combine with complexity measures
- • Translation Quality: Compare source and target texts
Best Practices and Tips
✅ Do
- • Use multiple metrics for comprehensive analysis
- • Consider text length when choosing measures
- • Account for genre and register differences
- • Preprocess text consistently across comparisons
- • Include adequate sample size (100+ words)
- • Document your preprocessing decisions
- • Compare texts from similar contexts
- • Validate findings with qualitative analysis
❌ Avoid
- • Relying solely on TTR for different text lengths
- • Comparing texts from different genres directly
- • Ignoring the impact of function words
- • Using inadequate sample sizes
- • Over-interpreting small differences
- • Mixing different preprocessing approaches
- • Assuming higher diversity is always better
- • Neglecting context and purpose
⚠️ Important Considerations
- • Lexical diversity measures vocabulary variation, not quality or appropriateness
- • Higher diversity isn't always better – context and purpose matter
- • Different measures may give different rankings for the same texts
- • Text preprocessing choices significantly affect results
- • Statistical significance testing may be needed for research applications
Frequently Asked Questions
Q: Which lexical diversity measure should I use?
The choice depends on your specific needs. Use TTR for quick comparisons of similar-length texts, MTLD for comparing texts of different lengths, MATTR for detailed local analysis, and HD-D for statistically rigorous research. Consider using multiple measures for comprehensive analysis.
Q: How does text length affect lexical diversity?
Longer texts typically show lower TTR because words naturally repeat more as text length increases. This is why measures like MTLD and MATTR were developed to be less sensitive to text length. For reliable comparisons, either use length-independent measures or ensure texts are similar lengths.
Q: Should I include function words in my analysis?
This depends on your research question. Including function words gives a complete picture of lexical use but may obscure content word diversity. Excluding them focuses on semantic vocabulary but loses information about syntactic complexity. Consider your specific goals and report your choice.
Q: What constitutes good lexical diversity?
“Good” diversity depends entirely on context. Academic writing typically shows higher diversity than conversation, but excessive diversity in simple instructions would be inappropriate. Consider your audience, purpose, and genre norms rather than pursuing maximum diversity.
Q: How can I improve the lexical diversity of my writing?
Use synonyms appropriately, vary sentence structures, employ precise vocabulary, avoid unnecessary repetition, and expand your vocabulary through reading. However, prioritize clarity and appropriateness over diversity – don't sacrifice communication for variation.
Q: Are these measures suitable for non-English texts?
While the mathematical principles apply to any language, interpretation may vary due to different morphological complexity, word formation processes, and cultural writing conventions. Be cautious when comparing across languages or using norms established for English.
Q: How much text do I need for reliable lexical diversity measurement?
Generally, at least 100 words are recommended for basic TTR analysis, while measures like MTLD require several hundred words for stability. Larger samples (1000+ words) provide more reliable estimates, especially for sophisticated measures like HD-D and Vocd-D.