En-Dictonary is a node.js module which makes works and their relations available as a package.
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.
You can install the package via bun or npm or yarn, along with one of the wordnet databases:
bun init
bun add -d @types/bun
bun install en-dictionary
vi index.ts
Once it has been added, you need to initialize the dictionary, like so:
const wordnet = require("en-wordnet").default;
const Dictionary = require("en-dictionary");
const start = async () => {
const dictionary = new Dictionary(wordnet.get("3.0"));
await dictionary.init();
let result = dictionary.searchFor(["yet"]);
console.log(result);
result = dictionary.searchFor(["preposterous"]);
console.log(result.get("preposterous").get("adjective"));
console.log(JSON.stringify(result.get("preposterous").get("adjective"), null, '\t'));
result = dictionary.searchSimpleFor(["preposterous"]);
console.log(result);
result = dictionary.wordsStartingWith("prestig");
console.log(result)
result = dictionary.wordsEndingWith("sterous");
console.log(result)
result = dictionary.wordsIncluding("grating");
console.log(result);
result = dictionary.wordsWithCharsIn("toaddndyrnrtssknwfsaregte");
console.log(result)
result = dictionary.wordsUsingAllCharactersFrom("indonesia");
console.log(result);
};
await start()
export {}And get started!
bun run index.tsThere 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:
bun test v1.1.3 (2615dc74)
src/index.test.ts:
✓ Test the index file for EnDictionary > Test initialization [4.28ms]
✓ Test that all POS are indexed > searchFor(["smart"]) returns the predicted result for adjective sense [0.45ms]
✓ Test that all POS are indexed > searchOffsetsInDataFor() can find the specified offset [0.17ms]
dist/index.test.js:
✓ Test the index file for EnDictionary > Test initialization [1.10ms]
✓ Test that all POS are indexed > searchFor(["smart"]) returns the predicted result for adjective sense [0.06ms]
✓ Test that all POS are indexed > searchOffsetsInDataFor() can find the specified offset [0.02ms]
src/reader/index.test.ts:
✓ Test the reader functionality > Test initialization [3.56ms]
src/database/index.test.ts:
[0.02ms] addIndex
✓ Test the dictionary > Test addIndex [3.55ms]
[0.01ms] indexLemmaSearch
[0.00ms] indexLemmaSearch2
✓ Test the dictionary > Test IndexLemmaSearch [0.29ms]
[0.10ms] indexOffsetSearch
[0.05ms] indexOffsetSearch2
✓ Test the dictionary > Test IndexOffsetSearch [0.97ms]
[0.04ms] addData
✓ Test the dictionary > Test addData [0.19ms]
[0.10ms] dataLemmaSearch
[0.01ms] dataLemmaSearch2
✓ Test the dictionary > Test DataLemmaSearch [0.31ms]
[0.01ms] dataOffsetSearch
[0.00ms] dataOffsetSearch2
✓ Test the dictionary > Test DataOffsetSearch [0.05ms]
src/parser/data.line.test.ts:
✓ Test parsing a data line > Parse a data line [0.12ms]
src/parser/index.line.test.ts:
✓ Test parsing an index line > Parse an index line [0.15ms]
src/dictionary/index.test.ts:
[0.04ms] search
[0.04ms] search2
✓ Test the dictionary > Test searchWord [1.43ms]
[0.01ms] searchOffsetsInData
✓ Test the dictionary > Test searchOffsetsInData [0.05ms]
[0.36ms] searchSimple-drink,train
✓ Test the dictionary > Test searchSimple [0.40ms]
[7.54ms] wordsStartingWith
✓ Test the dictionary > Test wordsStartingWith [7.59ms]
[6.32ms] wordsEndingWith
✓ Test the dictionary > Test wordsEndingWith [6.38ms]
[9.82ms] wordsIncluding
✓ Test the dictionary > Test wordsIncluding [9.87ms]
[164.22ms] wordsUsingAllCharactersFrom
✓ Test the dictionary > Test wordsUsingAllCharactersFrom [164.31ms]
[238.15ms] wordsWithCharsIn
[291.85ms] wordsWithCharsIn-priority
✓ Test the dictionary > Test wordsWithCharsIn [530.26ms]
✓ Test the dictionary > Test hasAllCharsIn [0.17ms]
✓ Test the dictionary > Test weird inputs [192.01ms]
dist/reader/index.test.js:
✓ Test the reader functionality > Test initialization [2.76ms]
dist/database/index.test.js:
[0.01ms] addIndex
✓ Test the dictionary > Test addIndex [1.17ms]
[0.01ms] indexLemmaSearch
[0.00ms] indexLemmaSearch2
✓ Test the dictionary > Test IndexLemmaSearch [0.09ms]
[0.10ms] indexOffsetSearch
[0.01ms] indexOffsetSearch2
✓ Test the dictionary > Test IndexOffsetSearch [0.24ms]
[0.01ms] addData
✓ Test the dictionary > Test addData [0.11ms]
[0.08ms] dataLemmaSearch
[0.01ms] dataLemmaSearch2
✓ Test the dictionary > Test DataLemmaSearch [0.22ms]
[0.00ms] dataOffsetSearch
[0.00ms] dataOffsetSearch2
✓ Test the dictionary > Test DataOffsetSearch [0.04ms]
dist/utils/index.test.js:
✓ Test Utils > Test getArray [0.30ms]
dist/parser/index.line.test.js:
✓ Test parsing an index line > Parse an index line [0.16ms]
dist/parser/data.line.test.js:
✓ Test parsing a data line > Parse a data line [0.10ms]
dist/dictionary/index.test.js:
[0.03ms] search
[0.03ms] search2
✓ Test the dictionary > Test searchWord [1.27ms]
[0.02ms] searchOffsetsInData
✓ Test the dictionary > Test searchOffsetsInData [0.06ms]
[0.30ms] searchSimple-drink,train
✓ Test the dictionary > Test searchSimple [0.33ms]
[8.55ms] wordsStartingWith
✓ Test the dictionary > Test wordsStartingWith [8.61ms]
[8.34ms] wordsEndingWith
✓ Test the dictionary > Test wordsEndingWith [8.43ms]
[9.07ms] wordsIncluding
✓ Test the dictionary > Test wordsIncluding [9.14ms]
[164.23ms] wordsUsingAllCharactersFrom
✓ Test the dictionary > Test wordsUsingAllCharactersFrom [164.31ms]
[264.27ms] wordsWithCharsIn
[277.32ms] wordsWithCharsIn-priority
✓ Test the dictionary > Test wordsWithCharsIn [541.73ms]
✓ Test the dictionary > Test hasAllCharsIn [0.09ms]
✓ Test the dictionary > Test weird inputs [226.36ms]
45 pass
0 fail
268 expect() calls
Ran 45 tests across 13 files. [6.86s]You can query for a single or multiple words with this syntax.
let result = dictionary.searchFor(["preposterous"]);
console.log(JSON.stringify(result.get("preposterous").get("adjective"), null, '\t'));Here's a sample outlet that you can expect for the queries above:
{
"lemma": "preposterous",
"pos": "adjective",
"offsetCount": 1,
"offsets": [
2570643
],
"offsetData": [
{
"offset": 2570643,
"pos": "adjective satellite",
"wordCount": 9,
"words": [
"absurd",
"cockeyed",
"derisory",
"idiotic",
"laughable",
"ludicrous",
"nonsensical",
"preposterous",
"ridiculous"
],
"pointerCnt": 5,
"pointers": [
{
"symbol": "Similar to",
"offset": 2570282,
"pos": "adjective"
},
{
"symbol": "Derivationally related form",
"offset": 6607809,
"pos": "noun"
},
{
"symbol": "Derivationally related form",
"offset": 852922,
"pos": "verb"
},
{
"symbol": "Derivationally related form",
"offset": 4891683,
"pos": "noun"
},
{
"symbol": "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
}
],
"pointerCount": 1,
"pointers": [
{
"symbol": "Similar to",
"offset": 0,
"pos": "adjective"
}
],
"senseCount": 1,
"tagSenseCount": 1,
"isComment": false
}There's also a simpler response version:
let result = dictionary.searchSimpleFor(["preposterous"]);
console.log(result);... which returns with a short and sweet
Map(1) {
"preposterous": Map(1) {
"adjective": {
words: "absurd, cockeyed, derisory, idiotic, laughable, ludicrous, nonsensical, preposterous, ridiculous",
meaning: "incongruous",
lemma: "preposterous",
},
},
}```
### 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:
```js
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" ][ "denigrating", "grating", "gratingly", "diffraction_grating", "grating", "integrating" ]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 with both a and b will show up on the topYou can expect the following output if you run the command above:
Map(10) {
"grandstander": Map(1) {
"noun": {
words: "grandstander",
meaning: "someone who performs with an eye to the applause from spectators in the grandstand",
lemma: "grandstander",
},
},
"transgressor": Map(1) {
"noun": {
words: "transgressor",
meaning: "someone who transgresses",
lemma: "transgressor",
},
},
"anterograde": Map(1) {
"adjective": {
words: "anterograde",
meaning: "of amnesia",
lemma: "anterograde",
},
},
"nonstandard": Map(1) {
"adjective": {
words: "nonstandard",
meaning: "not conforming to the language usage of a prestige group within a community",
lemma: "nonstandard",
},
},
"transgender": Map(1) {
"adjective": {
words: "transgender, transgendered",
meaning: "involving a partial or full reversal of gender",
lemma: "transgender",
},
},
"forwardness": Map(1) {
"noun": {
words: "bumptiousness, cockiness, pushiness, forwardness",
meaning: "offensive boldness and assertiveness",
lemma: "forwardness",
},
},
"nonattender": Map(1) {
"noun": {
words: "no-show, nonattender, truant",
meaning: "someone who shirks duty",
lemma: "nonattender",
},
},
"strangeness": Map(1) {
"noun": {
words: "unfamiliarity, strangeness",
meaning: "unusualness as a consequence of not being well known",
lemma: "strangeness",
},
},
"transferase": Map(1) {
"noun": {
words: "transferase",
meaning: "any of various enzymes that move a chemical group from one compound to another compound",
lemma: "transferase",
},
},
"retrograde": Map(2) {
"adjective": {
words: "retrograde",
meaning: "moving from east to west on the celestial sphere",
lemma: "retrograde",
},
"verb": {
words: "retrograde",
meaning: "move backward in an orbit, of celestial bodies",
lemma: "retrograde",
},
},
}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:
[
"conventionalised", "dimensional", "inconsiderable", "inconsiderate", "indonesian", "institutionalised",
"institutionalized", "insubordinate", "multidimensional", "noninstitutionalised", "noninstitutionalized",
"nonresidential", "unidimensional", "unimpassioned", "unsaponified", "inconsiderately", "abdominocentesis",
"animadversion", "antiredeposition", "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", "denationalise"
]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.
- 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