markovian-nlp
Advanced tools
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('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('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
In these examples, document is text from this source:
const document = 'That there is constant succession and flux of ideas in our minds...';
const oneSentence = sentences(document);
By default, a nondeterministic sentence is generated without providing a seed:
oneSentence();
// output: 'i have observed in the chief yet we might be able by a one...'
oneSentence();
// output: 'this is not angry chiding or so easy to them from running away...'
Optionally providing a seed generates a deterministic sentence:
oneSentence(1);
// output: 'i would promote introduce a constant succession and hindering the path...'
oneSentence(1);
// output: 'i would promote introduce a constant succession and hindering the path...'
Optionally providing a seed generates deterministic sentences:
sentences({
document,
count: 3,
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.) |
| user-defined parameter | type | implements | intermediate transformations |
|---|---|---|---|
document | String | compromise(document) | normalization, rule-based text parsing |
| 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 |
|---|---|---|---|---|---|
document, options.document | String | false | compromise(document) | Text. | |
seed, options.seed | Number | true | undefined | Chance(seed) | Leave undefined (default) for nondeterministic results, or specify seed for deterministic results. |
options | Object | true | |||
options.count | Number | true | 1 | Number of sentences to output. |
| 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 |
FAQs
NLP tools generate Markov sentences & models.
The npm package markovian-nlp receives a total of 14 weekly downloads. As such, markovian-nlp popularity was classified as not popular.
We found that markovian-nlp demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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