As an isomorphic JavaScript package, there are multiple ways for clients, servers, and bundlers to start using this library. Several methods do not require installation.
RunKit provides one of the least difficult ways to get started:
Declare imports in the JS
section to get started:
import {
ngramsDistribution,
sentences,
} from 'https://unpkg.com/markovian-nlp@latest?module';
const sentence = sentences({ document: 'oh me, oh my' });
console.log(sentence);
// example output: 'oh me oh me oh my'
Insert the following element within the <head>
tag of an HTML document:
<script src="https://unpkg.com/markovian-nlp@latest"></script>
After the script is loaded, the markovian
browser global is exposed:
const sentence = markovian.sentences({ document: 'oh me, oh my' });
console.log(sentence);
// example output: ['oh me oh me oh my']
With npm
installed, run terminal command:
npm i markovian-nlp
Once installed, declare method imports at the top of each JavaScript file they will be used.
Recommended
import {
ngramsDistribution,
sentences,
} from 'markovian-nlp';
const {
ngramsDistribution,
sentences,
} = require('markovian-nlp');
Generate text sentences from a Markov process.
Potential applications: Natural language generation
Optionally providing a seed
generates deterministic sentences.
In this example, document
is text from this source:
sentences({
count: 3,
document: 'That there is constant succession and flux of ideas in our minds...',
seed: 1,
});
// output: [
// 'i would promote introduce a constant succession and hindering the path...',
// 'he that train they seem to be glad to be done as may be avoided of our thoughts...',
// 'this wandering of attention and yet for ought i know this wandering thoughts i would promote...',
// ]
View the n-grams distribution of text.
Potential applications: Markov models
ngramsDistribution('birds have featured in culture and art since prehistoric times');
// output: {
// and: { _end: 0, _start: 0, art: 1 },
// art: { _end: 0, _start: 0, since: 1 },
// birds: { _end: 0, _start: 1, have: 1 },
// culture: { _end: 0, _start: 0, and: 1 },
// featured: { _end: 0, _start: 0, in: 1 },
// have: { _end: 0, _start: 0, featured: 1 },
// in: { _end: 0, _start: 0, culture: 1 },
// prehistoric: { _end: 0, _start: 0, times: 1 },
// since: { _end: 0, _start: 0, prehistoric: 1 },
// times: { _end: 1, _start: 0 },
// }
Each number represents the sum of occurrences.
startgram | endgram | bigrams |
---|---|---|
"birds" | "times" | all remaining keys ("have featured", "featured in", etc.) |
type | description |
---|---|
String | document (corpus or text) |
Object | ngramsDistribution (equivalent to identity , i.e.: this method's output) |
Array[Strings...] | combine multiple document |
Array[Objects...] | combine multiple ngramsDistribution |
Array[Strings, Objects...] | combine multiple document and ngramsDistribution |
type | description |
---|---|
Object | distributions of unigrams to startgrams, endgrams, and following bigrams |
// pseudocode signature representation (does not run)
ngramsDistribution(document) => ({
...unigrams: {
...{ ...bigram: bigramsDistribution },
_end: endgramsDistribution,
_start: startgramsDistribution,
},
});
user-defined parameter | type | optional | default value | implements | description |
---|---|---|---|---|---|
options.count |
Number | true | 1 |
Number of sentences to output. | |
options.distribution |
Object | required if options.document omitted |
n-grams distribution used in place of text. | ||
options.document |
String | required if options.distribution omitted |
compromise(document ) |
Text used in place of n-grams distribution. | |
options.seed |
Number | true | undefined |
Chance(seed ) |
Leave undefined (default) for nondeterministic results, or specify seed for deterministic results. |
type | description |
---|---|
Array[Strings...] | generated sentences |
Learn more about computational linguistics and natural language processing (NLP) on Wikipedia.
The following terms are used in the API documentation:
term | description |
---|---|
bigram | 2-gram sequence |
deterministic | repeatable, non-random |
endgram | final gram in a sequence |
n-gram | contiguous gram (word) sequence |
startgram | first gram in a sequence |
unigram | 1-gram sequence |