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OneLook's Reverse Dictionary seems to offer precisely the kind of tool you're looking for. However, I don't know that they're very good - I tried get on a plane, but board came back as result #96...
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#4: Attribution notice removed
by
System
·
2019-12-12T20:05:58Z (over 4 years ago)
Source: https://writers.stackexchange.com/a/3708 License name: CC BY-SA 3.0 License URL: https://creativecommons.org/licenses/by-sa/3.0/
#3: Attribution notice added
by
System
·
2019-12-08T01:52:06Z (over 4 years ago)
Source: https://writers.stackexchange.com/a/3708 License name: CC BY-SA 3.0 License URL: https://creativecommons.org/licenses/by-sa/3.0/
#2: Initial revision
[OneLook's Reverse Dictionary](http://www.onelook.com/reverse-dictionary.shtml) seems to offer precisely the kind of tool you're looking for. However, I don't know that they're very good - I tried `get on a plane,` but `board` came back as result #96, well after `slip` (#3), `touchdown` (#50), `precession of the equinoxes` (#66), and `fayez banihammad` (#85). From my superficial familiarity with computer language recognition, what you're describing is a pretty tough problem. Your intuitive definitions-of-unknown-words don't correspond to any existing searchable body of text, so language recognition would need to work based on the degree words are associated with each other, and that can get pretty wonky and unexpected. I don't see a better solution than asking human beings - [English.SE](http://english.stackexchange.com), for example, is probably great for word identification.
#1: Imported from external source
by
System
·
2011-08-24T10:26:08Z (over 12 years ago)
Original score: 6