Metrics for editing

Synonyms

Words with similar meaning. Use synonyms to find the most precise word for your idea or avoid excessive repetition. Synonyms on Expresso are pulled from WordNet corpus. Up to ten synonyms of the same part of speech are displayed for each word.

Examples: to showto point, to present, to indicate, to establish.

Weak verbs

Common English verbs with vague meaning. These words don’t trigger vivid imagery in readers’ heads and usually require additional words to deliver a meaningful message. Substituting weak verbs with more specific ones will liven most texts.

Units of measurement: percent of all verbs.

Examples: to be, to have, to do, to say, to go, to give.

Filler words

Unnecessary words which are common in spoken language. They often don’t add meaning and can be dropped.

Units of measurement: percent of all words.

Examples: very, much, just, actually, possibly, really.

Nominalizations

Complex nouns extended from shorter verbs, adjectives and nouns. They often slow a reader down and may be hard to interpret. Expresso finds them by specific suffixes. Try to rearrange the sentence to use the original shorter and more lively version of the word.

Units of measurement: percent of all nouns and non-possessive pronouns.

Examples: practicality, tendency, indication, analysis.

Entity substitutions

Pronouns and vague determiners common to spoken language. When overused, they can confuse a reader as the subject of the text would become more and more vague. If so, turn them into full entities with nouns, adjectives and adverbs.

Units of measurement: percent of all nouns and non-possessive pronouns.

Examples: it, this, him, there, thing, stuff.

Negations

Words and prefixes which reverse the meaning of the connected words. When used too often, especially more than once in a sentence, they slow a reader down and can obscure meaning. If so, replace negations with positive expressions with similar meaning.

Units of measurement: number per sentence.

Examples: not, no, nothing, to undo, to misspell.

Clustered nouns

Three or more consecutive nouns with, possibly, “of” in between. Such clusters are often hard to comprehend. If so, mix them with strong verbs and adjectives.

Units of measurement: percent of all nouns.

Examples: automated motor starting circuit, penicillin skin test result group.

Long noun phrases

Syntactic phrases with nouns at the head, which consist of 5 or more non-trivial words. Long noun phrases can be hard to understand. If so, drop non-essential descriptors or break into several phrases. Expresso finds noun phrases using spaCy syntactic dependency parser.

Units of measurement: percent of all noun phrases.

Example: my beautiful big excessively cheerful and friendly dog.

Passive voice

Sentences and clauses where the subject is the receiver of the main action. Often, sentences in passive voice sound weak and can be confusing. If so, rephrase the sentence in active voice.

Units of measurement: cases per sentence.

Examples: is made, is being carried, will be started.

Modals

Verb modifiers signifying ability or necessity. They can weaken statements by making them uncertain or too radical. If so, consider dropping the modal or substituting it with a relevant adverb.

Units of measurement: percent of all verbs.

Examples: must, would, should, may.

Rare words

Words outside of the 5000 most frequently used English words. To find them Expresso uses Corpus of Contemporary American English. Your readers may not be familiar with some rare words, which can drop text comprehension. If so, substitute rare words with more common synonyms.

Units of measurement: percent of all words.

Examples: to glare, to extort, an embassy, a bottleneck, pragmatism.

Extra long sentences

Sentences with 40 or more words. They might be too hard to understand. If so, break them down into several smaller sentences.

Units of measurement: percent of all sentences.

Example: “If, then, I were asked for the most important advice I could give, that which I considered to be the most useful to the men of our century, I should simply say: in the name of God, stop a moment, cease your work, look around you.” — Leo Tolstoy, Essays, Letters and Miscellanies

Extra short sentences

Sentences with 6 or fewer words. They might be too boring. If so, expand them by adding more detail.

Units of measurement: percent of all sentences.

Example: The day is warm.

Fragments

Incomplete sentences lacking a predicate. Fragments can be a powerful tool for setting tone and communicating feelings. They can also just be syntactic errors confusing to readers. If so, rephrase to add a predicate. Expresso finds fragments using spaCy syntactic dependency parser.

Units of measurement: percent of all sentences.

Examples: Wow! A nice day.

Clause-heavy sentences

Sentences with 4 or more clauses. Clause-heavy sentences might be hard to parse for readers. If so, break them down into several sentences with fewer clauses. Expresso finds clauses using spaCy syntactic dependency parser.

Units of measurement: percent of all sentences.

Example: The dog lived in the garden, but the cat, who was smarter, lived inside the house, which the dog was afraid to enter.

Late predicates

Predicates, which are more than 10 words deep into a sentence. We understand sentences by first finding a main predicate and then organizing other words according to their relationship to it. When a predicate comes late into a sentence, readers need to hold all previous words in working memory, which can be hard and confusing. If so, reorganize your sentence to move the predicate up. Expresso finds predicates using spaCy syntactic dependency parser.

Units of measurement: percent of all sentences.

Example: Alice, thinking it was very like having a game of play with a cart-horse, and expecting every moment to be trampled under its feet, ran round the thistle again. — Lewis Carroll, Alice's Adventures in Wonderland

Detached subjects

Subjects, which are more than 5 words apart from corresponding predicates. Sentences constructed in this way can be hard to parse. If so, reorganize them to move subjects and predicates closer together. Expresso finds subjects and predicates using spaCy syntactic dependency parser.

Units of measurement: percent of all sentences.

Example: Alice, thinking it was very like having a game of play with a cart-horse, and expecting every moment to be trampled under its feet, ran round the thistle again. — Lewis Carroll, Alice's Adventures in Wonderland

Frequent words

The most frequent words in the text excluding stopwords (described below). When clustered they can sound repetitive. If so, replace some of them with synonyms.

Units of measurement: number of occurrences.

Frequent bigrams

The most frequent pairs of words in the text excluding stopwords (described below). When clustered they can sound repetitive. If so, replace some of them with synonymous phrases.

Units of measurement: number of occurrences.

Frequent trigrams

The most frequent triplets of words in the text. When clustered they can sound repetitive. If so, replace some of them with synonymous phrases.

Units of measurement: number of occurrences.




General metrics

Vocabulary size

Number of different word lemmas in the text. Expresso finds lemmas of words via spaCy English lemmatizer.

Sentences

Number of sentences in the text. This metric highlights the first word in each sentence to easily see the pattern of sentence lengths.

Clauses per sentence

Average number of clauses per sentence. This metric highlights subjects and predicates of all clauses. Expresso finds clauses using spaCy syntactic dependency parser.

Predicate depth per sentence

Average position number of predicates from the beginning of a sentence. This metric highlights predicates of independent clauses in all sentences. Expresso finds predicates using spaCy syntactic dependency parser.

Units of measurement: words from the beginning of a sentence.

Syllables per word

Average number of syllables per word in the text. Expresso breaks words into syllables using Carnegie Mellon University Pronouncing Dictionary.

Readability grade

Comprehension level roughly corresponding to an American school grade. Expresso computes it using Flesch–Kincaid Grade Level formula. The grade is given only to texts with 100 or more words.

Parts of speech

Words of a particular part of speech in the text. Expresso puts all words into one of six categories: nouns, pronouns, verbs, adjectives, adverbs and other parts of speech. This categorization is done via spaCy part-of-speech tagger.

Units of measurement: percent of all words.

Sentence types by clause structure

Based on their clause structure sentences are classified into several types: simple, complex, compound and complex-compound.

Units of measurement: percent of all sentences.

Stopwords

Most common words not carrying text-specific information.

Units of measurement: percent of all words.

Examples: it, this, the, up, will.