README
En-Dictonary is a node.js module which makes works and their relations available as a package.
About
This packages uses the En-Wordnet package to make the words, their meanings and relationships available to your node.js package. It also adds helper functions for other ways to access the information.
Quick Start
You can install the package via npm
or yarn
yarn add en-dictionary
Once it has been added, you need to initialize the dictionary, like so
const wordnet = require('en-wordnet')
const Dictionary = require('./index')
const start = async () => {
const dictionary = new Dictionary(wordnet['3.0'])
await dictionary.init()
const result = dictionary.searchFor('yet')
}
start()
There are some more examples here.
The dictionary can take about 2000ms to load the data in memory, it doesn't use an external database/redis yet (nor is that planned, since most queries are fast enough, and the underlying data doesn't changes probably once a year)
As of version 1.2.0, most lookups are extremely fast
search: 1ms
search2: 0ms
searchOffsetsInData: 0ms
searchSimple-drink,train: 0ms
wordsStartingWith: 18ms
wordsEndingWith: 16ms
wordsIncluding: 17ms
wordsUsingAllCharactersFrom: 231ms
wordsWithCharsIn: 326ms
wordsWithCharsIn-priority: 350ms
addIndex: 0ms
indexLemmaSearch: 0ms
indexLemmaSearch2: 0ms
indexOffsetSearch: 0ms
indexOffsetSearch2: 0ms
addData: 0ms
dataLemmaSearch: 0ms
dataLemmaSearch2: 0ms
dataOffsetSearch: 0ms
dataOffsetSearch2: 0ms
Query words
You can query for a single word with this syntax. If you want to use multiple words, replace the
with _
.
let result = dict.searchFor('preposterous')
Here's a sample outlet that you can expect for the queries above
{
"preposterous": {
"lemma": "preposterous",
"pos": "adjective",
"offsetCount": 1,
"pointerCount": 1,
"pointers": [
"Similar to"
],
"senseCount": 1,
"tagSenseCount": 1,
"offsets": [
{
"offset": 2570643,
"pos": "ajective satellite",
"wordCount": 9,
"words": [
"absurd",
"cockeyed",
"derisory",
"idiotic",
"laughable",
"ludicrous",
"nonsensical",
"preposterous",
"ridiculous"
],
"pointerCnt": 5,
"pointers": [
{
"pointerSymbol": "Similar to",
"offset": 2570282,
"pos": "adjective"
},
{
"pointerSymbol": "Derivationally related form",
"offset": 6607809,
"pos": "noun"
},
{
"pointerSymbol": "Derivationally related form",
"offset": 852922,
"pos": "verb"
},
{
"pointerSymbol": "Derivationally related form",
"offset": 4891683,
"pos": "noun"
},
{
"pointerSymbol": "Derivationally related form",
"offset": 6607809,
"pos": "noun"
}
],
"glossary": [
"incongruous",
"inviting ridicule",
"\"the absurd excuse that the dog ate his homework\"",
"\"that's a cockeyed idea\"",
"\"ask a nonsensical question and get a nonsensical answer\"",
"\"a contribution so small as to be laughable\"",
"\"it is ludicrous to call a cottage a mansion\"",
"\"a preposterous attempt to turn back the pages of history\"",
"\"her conceited assumption of universal interest in her rather dull children was ridiculous\""
],
"isComment": false
}
],
"isComment": false
}
}
There's also a simpler response version
let result = dict.searchSimpleFor('preposterous')
... which returns with a short and sweet
{
"preposterous": {
"words": "absurd, cockeyed, derisory, idiotic, laughable, ludicrous, nonsensical, preposterous, ridiculous",
"meaning": "incongruous"
}
}
Find words which start with, end with or include a certain set of words
You can find words which start or end with a specific set of words, you can do this
let result = dict.wordsStartingWith('prestig')
result = dict.wordsEndingWith('sterous')
result = dict.wordsIncluding('grating')
Here's what you would get on running the functions above
[
"prestigious",
"prestige",
"prestigiousness"
]
[
"blusterous",
"boisterous",
"preposterous"
]
[
"gratingly",
"denigrating",
"grating",
"diffraction_grating",
"integrating"
]
Find words which can be created with a given set of words
This is useful when you're playing scrabble or a similar game. You can define the list of characters that you have available and the minimum length of the words that you need
let result = dict.wordsWithCharsIn('toaddndyrnrtssknwfsaregte')
let result = dict.wordsWithCharsIn('toaddndyrnrtssknwfsaregte', 'ab') // In this case words which both a and b will show up on the top
You can expect the following output if you run the command above
{
"transgressor": {
"words": "transgressor",
"meaning": "someone who transgresses"
},
"grandstander": {
"words": "grandstander",
"meaning": "someone who performs with an eye to the applause from spectators in the grandstand"
},
"nonattender": {
"words": "no-show, nonattender, truant",
"meaning": "someone who shirks duty"
},
"forwardness": {
"words": "readiness, eagerness, zeal, forwardness",
"meaning": "prompt willingness"
},
"anterograde": {
"words": "anterograde",
"meaning": "of amnesia"
},
"transferase": {
"words": "transferase",
"meaning": "any of various enzymes that move a chemical group from one compound to another compound"
},
"transgender": {
"words": "transgender, transgendered",
"meaning": "involving a partial or full reversal of gender"
},
"strangeness": {
"words": "unfamiliarity, strangeness",
"meaning": "unusualness as a consequence of not being well known"
},
"nonstandard": {
"words": "nonstandard",
"meaning": "not standard"
},
"waterfront": {
"words": "waterfront",
"meaning": "the area of a city (such as a harbor or dockyard) alongside a body of water"
}
}
Find words which have all of the words of a given word
This is sort of the opposite of what we did above
let result = dict.wordsUsingAllCharactersFrom('indonesia')
You can expect the following output if you run the command above
[
"abdominocentesis",
"inconsiderately",
"denationalise",
"conventionalised",
"animadversion",
"dimensional",
"antiredeposition",
"inconsiderable",
"inconsiderate",
"indonesian",
"institutionalised",
"institutionalized",
"insubordinate",
"multidimensional",
"noninstitutionalised",
"noninstitutionalized",
"nonresidential",
"unidimensional",
"unimpassioned",
"unsaponified",
"consideration",
"contradictoriness",
"decentalisation",
"decentralisation",
"decolonisation",
"decriminalisation",
"dehumanisation",
"demagnetisation",
"demineralisation",
"demonetisation",
"demonisation",
"denationalisation",
"denisonia",
"denominationalism",
"densification",
"depersonalisation",
"depersonalization",
"desalination",
"desalinisation",
"desalinization",
"desensitisation",
"desensitization",
"designation",
"destalinisation",
"destalinization",
"destination",
"desynchronisation",
"desynchronization",
"didanosine",
"dimensionality",
"disappointment",
"discontinuance",
"disinfestation",
"disintegration",
"disorientation",
"dispassionateness",
"dispensation",
"dissemination",
"extraordinariness",
"gymnadeniopsis",
"inconsiderateness",
"inconsideration",
"indonesia",
"indonesian",
"inordinateness",
"kinosternidae",
"modernisation",
"mountainside",
"ordinariness",
"predestination",
"predestinationist",
"pseudohallucination",
"reconsideration",
"sedimentation",
"superordination",
"tenderisation",
"underestimation"
]
Is this credible?
We currently rely on Version 3.0 of Princeton University's Wordnet, the data for which is available as a separate package. We will be adding more with time.
Credits
- TJ Holowaychuk for showing us how to use black and white beautifully to create the image on the top of the readme. Inspiration from apex/up
- Princeton Univerysity's Wordnet for bringing so much sanity in the world