Unique Word Counter
Count unique words and calculate vocabulary richness (type-token ratio) in text. Enter values for instant results with step-by-step formulas.
Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer
Unique Word Counter
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Formula: TTR = Unique Words (Types) / Total Words (Tokens)
Worked example — 10 unique words out of 15 total, TTR = 0.667 (High richness)
Formula
TTR = Unique Words (Types) / Total Words (Tokens)
The Type-Token Ratio divides the count of distinct words by the total word count. A ratio closer to 1.0 indicates higher vocabulary diversity. Hapax legomena count measures words appearing exactly once.
Worked Examples
Example 1: Analyzing a Short Paragraph
Problem:Analyze: 'The quick brown fox jumps over the lazy dog. The dog barked at the fox.'
Solution:Total words: 15 Unique words: 10 (the, quick, brown, fox, jumps, over, lazy, dog, barked, at) Repeated: 'the' (3x), 'fox' (2x), 'dog' (2x) Type-Token Ratio = 10/15 = 0.667 Hapax Legomena = 7 (quick, brown, jumps, over, lazy, barked, at) Vocabulary richness: High
Result:10 unique words out of 15 total, TTR = 0.667 (High richness)
Example 2: Comparing Two Writing Samples
Problem:Sample A: 'I like cats. I like dogs. I like birds.' vs Sample B: 'Felines prowl gracefully while canines frolic and songbirds chirp melodiously.'
Solution:Sample A: 7 total, 5 unique (I, like, cats, dogs, birds), TTR = 0.714 Sample B: 8 total, 8 unique, TTR = 1.000 Despite Sample A having high TTR, Sample B demonstrates superior vocabulary diversity with zero repetition and more sophisticated word choices.
Result:Sample B has perfect TTR of 1.0 vs Sample A at 0.714
Frequently Asked Questions
What is a unique word count and why does it matter?
A unique word count measures the number of distinct words in a text, regardless of how many times each word appears. For example, the sentence 'the cat sat on the mat' has 6 total words but only 5 unique words because 'the' appears twice. This metric is essential for writers, linguists, and content creators because it reveals vocabulary diversity and writing complexity. A higher unique word count relative to total words indicates richer vocabulary usage. Academic papers typically have higher vocabulary richness than casual blog posts. Content marketers use this metric to ensure their copy is varied and engaging rather than repetitive and monotonous.
What is the type-token ratio and how is it interpreted?
The type-token ratio (TTR) is a fundamental measure in computational linguistics calculated by dividing the number of unique words (types) by the total number of words (tokens). A TTR of 1.0 means every word is unique, while a lower ratio indicates more repetition. For typical English prose, a TTR between 0.4 and 0.6 is normal. Academic writing tends to score 0.5 to 0.7, while conversational speech may score 0.3 to 0.5. However, TTR is sensitive to text length because longer texts naturally have more repetition, which lowers the ratio. For comparing texts of different lengths, standardized TTR or moving-average TTR are more reliable alternatives.
What is a hapax legomenon and why is it significant?
A hapax legomenon (plural: hapax legomena) is a word that occurs only once within a given text or corpus. The term comes from Greek meaning 'something said only once.' In any natural language text, a large proportion of unique words will be hapax legomena, typically 40-60% of all unique words. This phenomenon follows Zipf's law, which states that word frequency is inversely proportional to its rank. Hapax legomena are significant in authorship attribution, language modeling, and vocabulary analysis. A high hapax ratio suggests the author uses many specialized or uncommon words, while a low ratio indicates reliance on a smaller, more frequently repeated vocabulary.
How does vocabulary richness differ across writing styles?
Vocabulary richness varies dramatically across different writing genres and purposes. Legal documents and scientific papers typically exhibit high vocabulary richness with TTR values of 0.55 to 0.70 due to specialized terminology and precise language requirements. Literary fiction ranges from 0.45 to 0.65, with authors like Shakespeare and James Joyce pushing the higher end. News articles tend toward 0.40 to 0.55 because journalistic style emphasizes clarity and repetition for readability. Social media posts and casual writing usually fall between 0.30 and 0.45. Children's literature deliberately uses lower vocabulary richness to match reading levels, typically scoring 0.25 to 0.40 depending on target age.
How can I improve the vocabulary richness of my writing?
Improving vocabulary richness requires deliberate practice and awareness of word choice patterns. Start by analyzing your current writing with a tool like this unique word counter to establish your baseline TTR. Read widely across genres to expose yourself to new words and phrases naturally. When editing, identify overused words and replace them with precise synonyms, but avoid using obscure words that sacrifice clarity for novelty. Use a thesaurus strategically to find alternatives for commonly repeated words like 'very,' 'good,' 'said,' and 'important.' Practice writing in different styles and genres to expand your active vocabulary. Remember that vocabulary richness should serve communication, not obscure meaning through unnecessarily complex language.
How many words are in different types of writing?
Word count benchmarks: tweet (280 characters ≈ 40–50 words), blog post (1,500–2,500 words), short story (1,000–7,500 words), novella (20,000–50,000 words), novel (70,000–100,000 words), academic essay (1,000–5,000 words), PhD thesis (80,000–100,000 words). Cover letters should be 250–400 words; resumes 400–800 words.
How many words per minute can the average person type?
Average typing speed is 40 words per minute (wpm) for adults; touch typists average 50–80 wpm. Professional typists reach 65–75 wpm. The world record exceeds 200 wpm. Smartphone thumb-typing averages 30–40 wpm. Testing regularly and practicing with specific problem keys is the fastest way to improve speed and accuracy.
How is speech time calculated from word count?
Divide word count by your speaking rate. Average conversational speech: 130–150 wpm. Presentations and public speaking: 120–150 wpm. Fast speaking: 160–180 wpm. A 10-minute speech at 130 wpm needs about 1,300 words; at 150 wpm, about 1,500 words. Practice delivery at your natural pace and measure actual time to calibrate.
How many words are on a typical page of text?
Word count per page depends heavily on formatting: a double-spaced 12pt Times New Roman academic page contains approximately 250–275 words, while a single-spaced page holds 500–550 words. A standard paperback novel page averages 250–300 words. Business documents in 11pt Calibri run about 400 words per single-spaced page. Online articles average 300–500 words per screen of content at standard column widths. For academic submissions that specify a page count, always confirm whether double- or single-spacing is required before converting from a word target.
What are standard word count requirements for academic writing?
Academic word count conventions vary by institution and level: undergraduate essays typically run 1,500–3,000 words, final-year dissertations 8,000–12,000 words, and master's theses 15,000–25,000 words. A PhD thesis in the UK is capped at 80,000 words by most universities (excluding references); US doctoral dissertations average 60,000–100,000 words. Abstracts are typically 150–300 words, and conference papers 5,000–8,000 words. When a word limit is given, the standard tolerance is ±10% — staying within this range ensures compliance without padding or excessive cutting.
References
Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer · Editorial policy
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