Reverse Dictionary Pdf

[Nikita Desjardins]

Asyou've probably noticed, words for "" are listed above. Hopefully the generated list of words for "" above suit your needs. If not, you might want to check out Related Words - another project of mine which uses a different technique (not though that it works best with single words, not phrases).

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.

Your list comprehension goes through all the dict's items finding all the matches, then just returns the first key. This generator expression will only iterate as far as necessary to return the first value:

This version is 26% shorter than yours but functions identically, even for redundant/ambiguous values (returns the first match, as yours does). However, it is probably twice as slow as yours, because it creates a list from the dict twice.

And if you prefer efficiency, @PaulMcGuire's approach is better. If there are lots of keys that share the same value it's more efficient not to instantiate that list of keys with a list comprehension and instead use use a generator:

No, you can not do this efficiently without looking in all the keys and checking all their values. So you will need O(n) time to do this. If you need to do a lot of such lookups you will need to do this efficiently by constructing a reversed dictionary (can be done also in O(n)) and then making a search inside of this reversed dictionary (each search will take on average O(1)).

Through values in dictionary can be object of any kind they can't be hashed or indexed other way. So finding key by the value is unnatural for this collection type. Any query like that can be executed in O(n) time only. So if this is frequent task you should take a look for some indexing of key like Jon sujjested or maybe even some spatial index (DB or ).

I'm using dictionaries as a sort of "database", so I need to find a key that I can reuse. For my case, if a key's value is None, then I can take it and reuse it without having to "allocate" another id. Just figured I'd share it.

I like this one because I don't have to try and catch any errors such as StopIteration or IndexError. If there's a key available, then free_id will contain one. If there isn't, then it will simply be None. Probably not pythonic, but I really didn't want to use a try here...

The comments include a number of old reference-librarian anecdotes. Of course, web search algorithms have been evolving towards the capabilities illustrated in the comic, as well as towards the heroic feats attributed to reference librarians.

This also reminds me of Michael Ramscar's ideas about the asymmetry between human word-to-concept and concept-to-word memory, which I featured in the discussion of an earlier comic ("Too much information", 1/14/2014), summarizing it like this:

[In looking up the term "reverse dictionary", I just learned that it's standard meaning is apparently "a dictionary alphabetized by the reversal of each entry", and what I've always meant by the term should be called a "conceptual dictionary".]

@Philip Taylor The customer has offended his spouse or partner in some way and wants to buy flowers for them but doesn't know which ones. The florist, an expert, instantly divines his intentions and makes a suitable recommendation.

I'm a bit skeptical of Wikipedia's assertion that "reverse dictionary" should be used exclusively to refer to works that list words spelled backwards. As the bibliography in the article on "Conceptual Dictionary" shows, most works refer to themselves as "reverses dictionaries. I welcome the suggestion that "conceptual dictionary" may add some clarity. But perhaps actual usage should be respected.

@Philip Taylor: Your question reminded me how, years ago, I was getting home from work at 8 or 9 in the evening and suddenly remembered that it was Valentine's Day. Luckily, there was a nearby Key Food that was open until 10, so I popped in and picked up a bunch of roses for, like, ten bucks. On my way home, a group of guys razzed me about the flowers I was holding, but I had a ready retort: "These flowers are going to save my a**."

I also remember the days when you could use your rotary phone to call the reference desk at the New York Public Library and ask just about any factual question and get an answer in short order. I assume that Google et al. killed this service long ago but don't really know.

With respect to what are the words in a thesaurus entry equivalent? You can't answer that question with another word; you have to say what it is. (Or describe what it is; it can't be a definition.) (It's probably a much easier question with the shops. Likewise with the differences among them (e.g., between a supermarket and a flower shop; what does the one you're looking for have that the other one doesn't?).) It's funny that we can't even know what our own thoughts are unless we use a category from the common language. Now I have to go and read Wittgenstein again.

There is a thriving "uh-oh, forgot" trade in flowers on 2/14. Experience teaches that supermarket flowers seem to be recognizable somehow. They are, however, marginally preferable to gas station flowers.

if you ask people to go from meanings to words (as in picture naming or category listing), average performance declines from the mid-30s on, with the decline accelerating through the 50s, 60s and 70s.

That skill is called word finding and I've definitely noticed more problems with it now that I'm in my mid-60s. Very frustrating at times, especially when I'm in a work meeting and struggling to recall a very common technical term.

Not by candlelight, though. When you're in the know, you make a point of setting the mood before proffering the bouquet. (Never heard of gas station flowers, but will investigate. If the price is right, one can always douse a candle or two.)

Back in the office I commented on this to a colleague and he saw the humour (lack of planning, etc) and added (clearly as a joke) that he intended to go to a petrol station on the way home to buy his wife flowers.

Tip 1: If a sign has more than two handshapes, locations, or sometimes handed types, try one of the two and then then the other. If a handshape has a gradient in movement between handshapes 0 and 5, try 5-half as in LEARN, EXPERIENCE, SLEEP, STORY.

If one or two hands are not in contact or near contact, use "neutral space". If the dominant hand is in contact (or close contact) with the base hand other than a specific listed handshape or palm, use "hand" (any handshape).

Select "3-close" for handshape, "repeat" for movement, "hand" for location, and "mirror" or two-handed symmetry. Sometimes there is a phonological variation (e.g. "U or H" handshape in RABBIT" so select another handshape to try.

Select "9-num" for handshape, "unidir" for movement, "9-num" for location, and "alternating" for handed. You will get "INFER". If "repeat" for movement, you will get "INTERPRET" as in "translate" and "INTERPRETER" in search result.

Most ASL dictionaries consist of fixated ASL signs (formal citation). That is roughly half of the actual language of ASL. The other half consists of inflections, classifier predicates, temporal aspects, determiners in variable space, spatial references, and more that you find in living language, not in dictionaries.

Another reason is that dictionaries show ASL signs/words in formal citation. The ASL production that you perceived may be informal. For example, the location of the ASL sign for "FOR" is on the upper side head. But, you might perceive a person's signing in informal register that the location is in the neutral space.

Use the citation (formal) form rather than the casual/informal form. For example, the ASL word/sign for know is on the upper head (temple) in citation form, but most signers would produce know on the lower part of the head in informal/casual register.

An ASL word that you may be looking up may be inflected in ASL sentence. ASL dictionary generally consists of "fixed" ASL glosses, but ASL has a rich complex grammar of inflections and classifier predicates that you don't find in dictionary.

A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. Our model comprises a sentence encoder and multiple predictors. The predictors are expected to identify different characteristics of the target word from the input query. We evaluate our model on English and Chinese datasets including both dictionary definitions and human-written descriptions. Experimental results show that our model achieves the state-of-the-art performance, and even outperforms the most popular commercial reverse dictionary system on the human-written description dataset. We also conduct quantitative analyses and a case study to demonstrate the effectiveness and robustness of our model. All the code and data of this work can be obtained on

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