Describing Words
This tool helps you find adjectives for things that you're trying to describe. Also check out ReverseDictionary.org and RelatedWords.org.
Click words for definitions.
Words to Describe queue
Below is a list of describing words for queue. You can sort the descriptive words by uniqueness or commonness using the button above. Sorry if there's a few unusual suggestions! The algorithm isn't perfect, but it does a pretty good job for most common nouns. Here's the list of words that can be used to describe queue:
- now straight and black
- rampant, double
- thick formal
- tight solid
- rampant double
- now straight
- severely braided
- tight but untidy
- fine clubbed
- day-long never-ending
- glossy braided
- sluggish endless
- stiff hessian
- orderly
- barely respectable
- long ratty
- launch-pad
- small and very precise
- long beribboned
- uncommonly neat
- large, stiff
- next shortest
- short braided
- usually tidy
- long, slow-moving
- long braided
- already disordered
- tight, short
- stiff old-fashioned
- tight, thick
- long, braided
- thick short
- thin, colorless
- usual huge
- beautiful silken
- white powdered
- long greasy
- braided black
- long military
- little straight
- long chinese
- long single
- clubbed
- almost horizontal
- outbound
- own thin
- broad, heavy
- long stiff
- neat dark
- long, thick
- smooth black
- own long
- seemingly infinite
- neat and tidy
- single or double
- drive-through
- braided
- powdered
- same thick
- almost endless
- long thick
- turned-up
- rather long
- immeasurable
- stiff little
- mile-long
- shortest
- proper military
- short, thick
- long, red
- long dark
- five-year
- almost silent
- disposal
- beribboned
- long thin
- rampant
- longest
- serpentine
- ratty
- long, heavy
- auburn
- hessian
- dislocated
- congested
- flaming red
- long
- single long
- long, black
- thick, dark
- long black
- ok
- modish
- much shorter
- well-behaved
- great long
- impeccable
- neat
- endless
- short
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Describing Words
The idea for the Describing Words engine came when I was building the engine for Related Words (it's like a thesaurus, but gives you a much broader set of related words, rather than just synonyms). While playing around with word vectors and the "HasProperty" API of conceptnet, I had a bit of fun trying to get the adjectives which commonly describe a word. Eventually I realised that there's a much better way of doing this: parse books!
Project Gutenberg was the initial corpus, but the parser got greedier and greedier and I ended up feeding it somewhere around 100 gigabytes of text files - mostly fiction, including many contemporary works. The parser simply looks through each book and pulls out the various descriptions of nouns.
Hopefully it's more than just a novelty and some people will actually find it useful for their writing and brainstorming, but one neat little thing to try is to compare two nouns which are similar, but different in some significant way - for example, gender is interesting: "woman" versus "man" and "boy" versus "girl". On an inital quick analysis it seems that authors of fiction are at least 4x more likely to describe women (as opposed to men) with beauty-related terms (regarding their weight, features and general attractiveness). In fact, "beautiful" is possibly the most widely used adjective for women in all of the world's literature, which is quite in line with the general unidimensional representation of women in many other media forms. If anyone wants to do further research into this, let me know and I can give you a lot more data (for example, there are about 25000 different entries for "woman" - too many to show here).
The blueness of the results represents their relative frequency. You can hover over an item for a second and the frequency score should pop up. The "uniqueness" sorting is default, and thanks to my Complicated Algorithm™, it orders them by the adjectives' uniqueness to that particular noun relative to other nouns (it's actually pretty simple). As you'd expect, you can click the "Sort By Usage Frequency" button to adjectives by their usage frequency for that noun.
Special thanks to the contributors of the open-source mongodb which was used in this project.
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