Word for an old building

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.

Please note that Describing Words uses third party scripts (such as Google Analytics and advertisements) which use cookies. To learn more, see the privacy policy.

The way Reverse Dictionary works is pretty simple. It simply looks through tonnes of dictionary definitions and grabs the ones that most closely match your search query. For example, if you type something like «longing for a time in the past», then the engine will return «nostalgia». The engine has indexed several million definitions so far, and at this stage it’s starting to give consistently good results (though it may return weird results sometimes). It acts a lot like a thesaurus except that it allows you to search with a definition, rather than a single word. So in a sense, this tool is a «search engine for words», or a sentence to word converter.

I made this tool after working on Related Words which is a very similar tool, except it uses a bunch of algorithms and multiple databases to find similar words to a search query. That project is closer to a thesaurus in the sense that it returns synonyms for a word (or short phrase) query, but it also returns many broadly related words that aren’t included in thesauri. So this project, Reverse Dictionary, is meant to go hand-in-hand with Related Words to act as a word-finding and brainstorming toolset. For those interested, I also developed Describing Words which helps you find adjectives and interesting descriptors for things (e.g. waves, sunsets, trees, etc.).

In case you didn’t notice, you can click on words in the search results and you’ll be presented with the definition of that word (if available). The definitions are sourced from the famous and open-source WordNet database, so a huge thanks to the many contributors for creating such an awesome free resource.

Special thanks to the contributors of the open-source code that was used in this project: Elastic Search, @HubSpot, WordNet, and @mongodb.

Please note that Reverse Dictionary uses third party scripts (such as Google Analytics and advertisements) which use cookies. To learn more, see the privacy policy.