melchior-natural
Advanced tools
General natural language (tokenizing, stemming (English, Russian, Spanish), classification, inflection, phonetics, tfidf, WordNet, jaro-winkler, Levenshtein distance, Dice's Coefficient) facilities for node.
"Natural" is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.
It's still in the early stages, so we're very interested in bug reports, contributions and the like.
Note that many algorithms from Rob Ellis's node-nltools are being merged into this project and will be maintained from here onward.
At the moment, most of the algorithms are English-specific, but in the long-term, some diversity will be in order. Thanks to Polyakov Vladimir, Russian stemming has been added!, Thanks to David Przybilla, Spanish stemming has been added!.
If you're just looking to use natural without your own node application, you can install via NPM like so:
npm install natural
If you're interested in contributing to natural, or just hacking on it, then by all means fork away!
Word, Regexp, and Treebank tokenizers are provided for breaking text up into arrays of tokens:
var natural = require('natural'),
tokenizer = new natural.WordTokenizer();
console.log(tokenizer.tokenize("your dog has fleas."));
// [ 'your', 'dog', 'has', 'fleas' ]
The other tokenizers follow a similar pattern:
tokenizer = new natural.TreebankWordTokenizer();
console.log(tokenizer.tokenize("my dog hasn't any fleas."));
// [ 'my', 'dog', 'has', 'n\'t', 'any', 'fleas', '.' ]
tokenizer = new natural.RegexpTokenizer({pattern: /\-/});
console.log(tokenizer.tokenize("flea-dog"));
// [ 'flea', 'dog' ]
tokenizer = new natural.WordPunctTokenizer();
console.log(tokenizer.tokenize("my dog hasn't any fleas."));
// [ 'my', 'dog', 'hasn', '\'', 't', 'any', 'fleas', '.' ]
Natural provides an implementation of the Jaro–Winkler string distance measuring algorithm. This will return a number between 0 and 1 which tells how closely the strings match (0 = not at all, 1 = exact match):
var natural = require('natural');
console.log(natural.JaroWinklerDistance("dixon","dicksonx"))
console.log(natural.JaroWinklerDistance('not', 'same'));
Output:
0.7466666666666666
0
Natural also offers support for Levenshtein distances:
var natural = require('natural');
console.log(natural.LevenshteinDistance("ones","onez"));
console.log(natural.LevenshteinDistance('one', 'one'));
Output:
1
0
The cost of the three edit operations are modifiable for Levenshtein:
console.log(natural.LevenshteinDistance("ones","onez", {
insertion_cost: 1,
deletion_cost: 1,
substitution_cost: 1
}));
Output:
1
And Dice's co-efficient:
var natural = require('natural');
console.log(natural.DiceCoefficient('thing', 'thing'));
console.log(natural.DiceCoefficient('not', 'same'));
Output:
1
0
Currently stemming is supported via the Porter and Lancaster (Paice/Husk) algorithms.
var natural = require('natural');
This example uses a Porter stemmer. "word" is returned.
console.log(natural.PorterStemmer.stem("words")); // stem a single word
in Russian:
console.log(natural.PorterStemmerRu.stem("падший"));
in Spanish:
console.log(natural.PorterStemmerEs.stem("jugaría"));
attach() patches stem() and tokenizeAndStem() to String as a shortcut to
PorterStemmer.stem(token). tokenizeAndStem() breaks text up into single words
and returns an array of stemmed tokens.
natural.PorterStemmer.attach();
console.log("i am waking up to the sounds of chainsaws".tokenizeAndStem());
console.log("chainsaws".stem());
the same thing can be done with a Lancaster stemmer:
natural.LancasterStemmer.attach();
console.log("i am waking up to the sounds of chainsaws".tokenizeAndStem());
console.log("chainsaws".stem());
Two classifiers are currently supported, Naive Bayes and logistic regression. The following examples use the BayesClassifier class, but the LogisticRegressionClassifier class could be substituted instead.
var natural = require('natural'),
classifier = new natural.BayesClassifier();
You can train the classifier on sample text. It will use reasonable defaults to tokenize and stem the text.
classifier.addDocument('i am long qqqq', 'buy');
classifier.addDocument('buy the q\'s', 'buy');
classifier.addDocument('short gold', 'sell');
classifier.addDocument('sell gold', 'sell');
classifier.train();
Outputs "sell"
console.log(classifier.classify('i am short silver'));
Outputs "buy"
console.log(classifier.classify('i am long copper'));
You have access to the set of matched classes and the associated value from the classifier.
Outputs:
[ { label: 'buy', value: 0.39999999999999997 },
{ label: 'sell', value: 0.19999999999999998 } ]
From this:
console.log(classifier.getClassifications('i am long copper'));
The classifier can also be trained with and can classify arrays of tokens, strings, or any mixture of the two. Arrays let you use entirely custom data with your own tokenization/stemming, if you choose to implement it.
classifier.addDocument(['sell', 'gold'], 'sell');
The training process can be monitored by subscribing to the event trainedWithDocument that's emitted by the classifier, this event's emitted each time a document is finished being trained against:
classifier.events.on('trainedWithDocument', function (obj) {
console.log(obj);
/* {
* total: 23 // There are 23 total documents being trained against
* index: 12 // The index/number of the document that's just been trained against
* doc: {...} // The document that has just been indexed
* }
*/
});
A classifier can also be persisted and recalled so you can reuse a training
classifier.save('classifier.json', function(err, classifier) {
// the classifier is saved to the classifier.json file!
});
To recall from the classifier.json saved above:
natural.BayesClassifier.load('classifier.json', null, function(err, classifier) {
console.log(classifier.classify('long SUNW'));
console.log(classifier.classify('short SUNW'));
});
A classifier can also be serialized and deserialized like so:
var classifier = new natural.BayesClassifier();
classifier.addDocument(['sell', 'gold'], 'sell');
classifier.addDocument(['buy', 'silver'], 'buy');
// serialize
var raw = JSON.stringify(classifier);
// deserialize
var restoredClassifier = natural.BayesClassifier.restore(JSON.parse(raw));
console.log(restoredClassifier.classify('i should sell that'));
Phonetic matching (sounds-like) matching can be done with the SoundEx, Metaphone or DoubleMetaphone algorithms
var natural = require('natural'),
metaphone = natural.Metaphone, soundEx = natural.SoundEx;
var wordA = 'phonetics';
var wordB = 'fonetix';
To test the two words to see if they sound alike:
if(metaphone.compare(wordA, wordB))
console.log('they sound alike!');
The raw phonetics are obtained with process():
console.log(metaphone.process('phonetics'));
A maximum code length can be supplied:
console.log(metaphone.process('phonetics', 3));
DoubleMetaphone deals with two encodings returned in an array. This
feature is experimental and subject to change:
var natural = require('natural'),
dm = natural.DoubleMetaphone;
var encodings = dm.process('Matrix');
console.log(encodings[0]);
console.log(encodings[1]);
Attaching will patch String with useful methods:
metaphone.attach();
soundsLike is essentially a shortcut to Metaphone.compare:
if(wordA.soundsLike(wordB))
console.log('they sound alike!');
The raw phonetics are obtained with phonetics():
console.log('phonetics'.phonetics());
Full text strings can be tokenized into arrays of phonetics (much like how tokenization-to-arrays works for stemmers):
console.log('phonetics rock'.tokenizeAndPhoneticize());
Same module operations applied with SoundEx:
if(soundEx.compare(wordA, wordB))
console.log('they sound alike!');
The same String patches apply with soundEx:
soundEx.attach();
if(wordA.soundsLike(wordB))
console.log('they sound alike!');
console.log('phonetics'.phonetics());
Nouns can be pluralized/singularized with a NounInflector:
var natural = require('natural'),
nounInflector = new natural.NounInflector();
To pluralize a word (outputs "radii"):
console.log(nounInflector.pluralize('radius'));
To singularize a word (outputs "beer"):
console.log(nounInflector.singularize('beers'));
Like many of the other features, String can be patched to perform the operations directly. The "Noun" suffix on the methods is necessary, as verbs will be supported in the future.
nounInflector.attach();
console.log('radius'.pluralizeNoun());
console.log('beers'.singularizeNoun());
Numbers can be counted with a CountInflector:
var countInflector = natural.CountInflector;
Outputs "1st":
console.log(countInflector.nth(1));
Outputs "111th":
console.log(countInflector.nth(111));
Present Tense Verbs can be pluralized/singularized with a PresentVerbInflector. This feature is still experimental as of 0.0.42, so use with caution, and please provide feedback.
var verbInflector = new natural.PresentVerbInflector();
Outputs "becomes":
console.log(verbInflector.singularize('become'));
Outputs "become":
console.log(verbInflector.pluralize('becomes'));
Like many other natural modules, attach() can be used to patch strings with
handy methods.
verbInflector.attach();
console.log('walk'.singularizePresentVerb());
console.log('walks'.pluralizePresentVerb());
n-grams can be obtained for either arrays or strings (which will be tokenized for you):
var NGrams = natural.NGrams;
console.log(NGrams.bigrams('some words here'));
console.log(NGrams.bigrams(['some', 'words', 'here']));
Both of the above output: [ [ 'some', 'words' ], [ 'words', 'here' ] ]
console.log(NGrams.trigrams('some other words here'));
console.log(NGrams.trigrams(['some', 'other', 'words', 'here']));
Both of the above output: [ [ 'some', 'other', 'words' ], [ 'other', 'words', 'here' ] ]
console.log(NGrams.ngrams('some other words here for you', 4));
console.log(NGrams.ngrams(['some', 'other', 'words', 'here', 'for',
'you'], 4));
The above outputs: [ [ 'some', 'other', 'words', 'here' ], [ 'other', 'words', 'here', 'for' ], [ 'words', 'here', 'for', 'you' ] ]
n-grams can also be returned with left or right padding by passing a start and/or end symbol to the bigrams, trigrams or ngrams.
console.log(NGrams.ngrams('some other words here for you', 4, '[start]', '[end]'));
The above will output:
[ [ '[start]', '[start]', '[start]', 'some' ],
[ '[start]', '[start]', 'some', 'other' ],
[ '[start]', 'some', 'other', 'words' ],
[ 'some', 'other', 'words', 'here' ],
[ 'other', 'words', 'here', 'for' ],
[ 'words', 'here', 'for', 'you' ],
[ 'here', 'for', 'you', '[end]' ],
[ 'for', 'you', '[end]', '[end]' ],
[ 'you', '[end]', '[end]', '[end]' ] ]
For only end symbols, pass null for the start symbol, for instance:
console.log(NGrams.ngrams('some other words here for you', 4, null, '[end]'));
Will output:
[ [ 'some', 'other', 'words', 'here' ],
[ 'other', 'words', 'here', 'for' ],
[ 'words', 'here', 'for', 'you' ],
[ 'here', 'for', 'you', '[end]' ],
[ 'for', 'you', '[end]', '[end]' ],
[ 'you', '[end]', '[end]', '[end]' ] ]
For Chinese like languages, you can use NGramsZH to do a n-gram, and all apis are the same:
var NGramsZH = natural.NGramsZH;
console.log(NGramsZH.bigrams('中文测试'));
console.log(NGramsZH.bigrams(['中', '文', '测', '试']));
console.log(NGramsZH.trigrams('中文测试'));
console.log(NGramsZH.trigrams(['中', '文', '测', '试']));
console.log(NGramsZH.ngrams('一个中文测试', 4));
console.log(NGramsZH.ngrams(['一', '个', '中', '文', '测',
'试'], 4));
Term Frequency–Inverse Document Frequency (tf-idf) is implemented to determine how important a word (or words) is to a document relative to a corpus. The following example will add four documents to a corpus and determine the weight of the word "node" and then the weight of the word "ruby" in each document.
var natural = require('natural'),
TfIdf = natural.TfIdf,
tfidf = new TfIdf();
tfidf.addDocument('this document is about node.');
tfidf.addDocument('this document is about ruby.');
tfidf.addDocument('this document is about ruby and node.');
tfidf.addDocument('this document is about node. it has node examples');
console.log('node --------------------------------');
tfidf.tfidfs('node', function(i, measure) {
console.log('document #' + i + ' is ' + measure);
});
console.log('ruby --------------------------------');
tfidf.tfidfs('ruby', function(i, measure) {
console.log('document #' + i + ' is ' + measure);
});
The above outputs:
node --------------------------------
document #0 is 1
document #1 is 0
document #2 is 1
document #3 is 2
ruby --------------------------------
document #0 is 0
document #1 is 1.2876820724517808
document #2 is 1.2876820724517808
document #3 is 0
This approach can also be applied to individual documents.
The following example measures the term "node" in the first and second documents.
console.log(tfidf.tfidf('node', 0));
console.log(tfidf.tfidf('node', 1));
A TfIdf instance can also load documents from files on disk.
var tfidf = new TfIdf();
tfidf.addFileSync('data_files/one.txt');
tfidf.addFileSync('data_files/two.txt');
Multiple terms can be measured as well, with their weights being added into a single measure value. The following example determines that the last document is the most relevant to the words "node" and "ruby".
var natural = require('natural'),
TfIdf = natural.TfIdf,
tfidf = new TfIdf();
tfidf.addDocument('this document is about node.');
tfidf.addDocument('this document is about ruby.');
tfidf.addDocument('this document is about ruby and node.');
tfidf.tfidfs('node ruby', function(i, measure) {
console.log('document #' + i + ' is ' + measure);
});
The above outputs:
document #0 is 1
document #1 is 1
document #2 is 2
The examples above all use strings, which case natural to automatically tokenize the input. If you wish to perform your own tokenization or other kinds of processing, you can do so, then pass in the resultant arrays later. This approach allows you to bypass natural's default preprocessing.
var natural = require('natural'),
TfIdf = natural.TfIdf,
tfidf = new TfIdf();
tfidf.addDocument(['document', 'about', 'node']);
tfidf.addDocument(['document', 'about', 'ruby']);
tfidf.addDocument(['document', 'about', 'ruby', 'node']);
tfidf.addDocument(['document', 'about', 'node', 'node', 'examples']);
tfidf.tfidfs(['node', 'ruby'], function(i, measure) {
console.log('document #' + i + ' is ' + measure);
});
It's possible to retrieve a list of all terms in a document, sorted by their importance.
tfidf.listTerms(0 /*document index*/).forEach(function(item) {
console.log(item.term + ': ' + item.tfidf);
});
A TfIdf instance can also be serialized and deserialized for save and recall.
var tfidf = new TfIdf();
tfidf.addDocument('document one', 'un');
tfidf.addDocument('document Two', 'deux');
var s = JSON.stringify(tfidf);
// save "s" to disk, database or otherwise
// assuming you pulled "s" back out of storage.
var tfidf = new TfIdf(JSON.parse(s));
Tries are a very efficient data structure used for prefix-based searches. Natural comes packaged with a basic Trie implementation which can support match collection along a path, existence search and prefix search.
You need to add words to build up the dictionary of the Trie, this is an example of basic Trie set up:
var natural = require('natural'),
Trie = natural.Trie;
var trie = new Trie();
// Add one string at a time
trie.addString("test");
// Or add many strings
trie.addStrings(["string1", "string2", "string3"]);
The most basic operation on a Trie is to see if a search string is marked as a word in the Trie.
console.log(trie.contains("test")); // true
console.log(trie.contains("asdf")); // false
The find prefix search will find the longest prefix that is identified as a word in the trie. It will also return the remaining portion of the string which it was not able to match.
console.log(trie.findPrefix("tester")); // ['test', 'er']
console.log(trie.findPrefix("string4")); // [null, '4']
console.log(trie.findPrefix("string3")); // ['string3', '']
This search will return all prefix matches along the search string path.
trie.addString("tes");
trie.addString("est");
console.log(trie.findMatchesOnPath("tester")); // ['tes', 'test'];
This search will return all of the words in the Trie with the given prefix, or [ ] if not found.
console.log(trie.keysWithPrefix("string")); // ["string1", "string2", "string3"]
By default the trie is case-sensitive, you can use it in case-_in_sensitive mode by passing false
to the Trie constructor.
trie.contains("TEST"); // false
var ciTrie = new Trie(false);
ciTrie.addString("test");
ciTrie.contains("TEsT"); // true
In the case of the searches which return strings, all strings returned will be in lower case if you are in case-_in_sensitive mode.
EdgeWeightedDigraph represents a digraph, you can add an edge, get the number vertexes, edges, get all edges and use toString to print the Digraph.
initialize a digraph:
var EdgeWeightedDigraph = natural.EdgeWeightedDigraph;
var digraph = new EdgeWeightedDigraph();
digraph.add(5,4,0.35);
digraph.add(5,1,0.32);
digraph.add(1,3,0.29);
digraph.add(6,2,0.40);
digraph.add(3,6,0.52);
digraph.add(6,4,0.93);
the api used is: add(from, to, weight).
get the number of vertexes:
console.log(digraph.v());
you will get 5.
get the number of edges:
console.log(digraph.e());
you will get 5.
ShortestPathTree represents a data type for solving the single-source shortest paths problem in edge-weighted directed acyclic graphs (DAGs). The edge weights can be positive, negative, or zero. There are three APIs: getDistTo(vertex), hasPathTo(vertex), pathTo(vertex).
var ShortestPathTree = natural.ShortestPathTree;
var spt = new ShortestPathTree(digraph, 5);
digraph is an instance of EdgeWeightedDigraph, the second param is the start vertex of DAG.
Will return the dist to vertex.
console.log(spt.getDistTo(4));
the output will be: 0.35
console.log(spt.hasDistTo(4));
console.log(spt.hasDistTo(5));
output will be:
true
false
this will return a shortest path:
console.log(spt.pathTo(4));
output will be:
[5, 4]
LongestPathTree represents a data type for solving the single-source shortest paths problem in edge-weighted directed acyclic graphs (DAGs). The edge weights can be positive, negative, or zero. There are three APIs same as ShortestPathTree: getDistTo(vertex), hasPathTo(vertex), pathTo(vertex).
var ShortestPathTree = natural.ShortestPathTree;
var spt = new ShortestPathTree(digraph, 5);
digraph is an instance of EdgeWeightedDigraph, the second param is the start vertex of DAG.
Will return the dist to vertex.
console.log(spt.getDistTo(4));
the output will be: 2.06
console.log(spt.hasDistTo(4));
console.log(spt.hasDistTo(5));
output will be:
true
false
this will return a shortest path:
console.log(spt.pathTo(4));
output will be:
[5, 1, 3, 6, 4]
One of the newest and most experimental features in natural is WordNet integration. Here's an example of using natural to look up definitions of the word node. To use the WordNet module, first install the WordNet database files using wordnet-db:
npm install wordnet-db
Keep in mind that the WordNet integration is to be considered experimental at this point, and not production-ready. The API is also subject to change. For an implementation with vastly increased performance, as well as a command-line interface, see wordpos.
Here's an example of looking up definitions for the word "node".
var wordnet = new natural.WordNet();
wordnet.lookup('node', function(results) {
results.forEach(function(result) {
console.log('------------------------------------');
console.log(result.synsetOffset);
console.log(result.pos);
console.log(result.lemma);
console.log(result.synonyms);
console.log(result.pos);
console.log(result.gloss);
});
});
Given a synset offset and a part of speech, a definition can be looked up directly.
var wordnet = new natural.WordNet();
wordnet.get(4424418, 'n', function(result) {
console.log('------------------------------------');
console.log(result.lemma);
console.log(result.pos);
console.log(result.gloss);
console.log(result.synonyms);
});
If you have manually downloaded the WordNet database files, you can pass the folder to the constructor:
var wordnet = new natural.WordNet('/my/wordnet/dict');
As of v0.1.11, WordNet data files are no longer automatically downloaded.
Princeton University "About WordNet." WordNet. Princeton University. 2010. http://wordnet.princeton.edu
A probabilistic spellchecker based on http://norvig.com/spell-correct.html
This is best constructed with an array of tokens from a corpus, but a simple list of words from a dictionary will work.
var corpus = ['something', 'soothing'];
var spellcheck = new Spellcheck(corpus);
It uses the trie datastructure for fast boolean lookup of a word
spellcheck.isCorrect('cat'); // false
It suggests corrections (sorted by probability in descending order) that are up to a maximum edit distance away from the input word. According to Norvig, a max distance of 1 will cover 80% to 95% of spelling mistakes. After a distance of 2, it becomes very slow.
spellcheck.getCorrections('soemthing', 1); // ['something']
spellcheck.getCorrections('soemthing', 2); // ['something', 'soothing']
This is a part-of-speech tagger based on Eric Brill's transformational algorithm. Transformation rules are specified in external files.
var natural = require("./lib/natural");
var base_folder = "some_path/lib/natural/brill_pos_tagger";
var rulesFilename = base_folder + "/data/tr_from_posjs.txt";
var lexiconFilename = base_folder + "/data/lexicon_from_posjs.json";
var defaultCategory = 'N';
var lexicon = new natural.Lexicon(lexiconFilename, defaultCategory);
var rules = new natural.Ruleset(rulesFilename);
var tagger = new natural.BrillPOSTagger(lexicon, rules);
var sentence = ["I", "see", "the", "man", "with", "the", "telescope"];
console.log(JSON.stringify(tagger.tag(sentence)));
The lexicon is either a JSON file that has the following structure:
{
"word1": ["cat1"],
"word2": ["cat2", "cat3"],
...
}
or a text file:
word1 cat1 cat2
word2 cat3
...
Words may have multiple categories in the lexicon file. The tagger uses only the first category specified.
Transformation rules are specified as follows:
OLD_CAT NEW_CAT PREDICATE PARAMETER
This means that if the category of the current position is OLD_CAT and the predicate is true, the category is replaced by NEW_CAT. The predicate may use the parameter in different ways: sometimes the parameter is used for specifying the outcome of the predicate:
NN CD CURRENT-WORD-IS-NUMBER YES
This means that if the outcome of predicate CURRENT-WORD-IS-NUMBER is YES, the
category is replaced by CD.
The parameter can also be used to check the category of a word in the sentence:
VBD NN PREV-TAG DT
Here the category of the previous word must be DT for the rule to be applied.
The tagger applies transformation rules that may change the category of words. The input sentence must be split into words which are assigned with categories. The tagged sentence is then processed from left to right. At each step all rules are applied once; rules are applied in the order in which they are specified. Algorithm:
function(sentence) {
var tagged_sentence = new Array(sentence.length);
// snip
// Apply transformation rules
for (var i = 0, size = sentence.length; i < size; i++) {
this.transformation_rules.forEach(function(rule) {
rule.apply(tagged_sentence, i);
});
}
return(tagged_sentence);
}
Predicates are defined in module lib/Predicate.js. In that file
a function must be created that serves as predicate. A predicate accepts a
tagged sentence, the current position in the sentence that should be tagged, and
the
outcome(s) of the predicate. An example of a predicate that checks the category of the current word:
function current_word_is_tag(tagged_sentence, i, parameter) {
return(tagged_sentence[i][0] === parameter);
}
Some predicates accept two parameters. Next step is to map a keyword to this predicate so that it can be used in the transformation rules. The mapping is also defined in lib/Predicate.js:
var predicates = {
"CURRENT-WORD-IS-TAG": current_word_is_tag,
"PREV-WORD-IS-CAP": prev_word_is_cap
}
When developing, please:
The current configuration of the unit tests requires the following environment variable to be set:
export NODE_PATH=.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
This license is available as the file LICENSE in any downloaded version of WordNet. WordNet 3.0 license: (Download)
WordNet Release 3.0 This software and database is being provided to you, the LICENSEE, by Princeton University under the following license. By obtaining, using and/or copying this software and database, you agree that you have read, understood, and will comply with these terms and conditions.: Permission to use, copy, modify and distribute this software and database and its documentation for any purpose and without fee or royalty is hereby granted, provided that you agree to comply with the following copyright notice and statements, including the disclaimer, and that the same appear on ALL copies of the software, database and documentation, including modifications that you make for internal use or for distribution. WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved. THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. The name of Princeton University or Princeton may not be used in advertising or publicity pertaining to distribution of the software and/or database. Title to copyright in this software, database and any associated documentation shall at all times remain with Princeton University and LICENSEE agrees to preserve same.
FAQs
General natural language (tokenizing, stemming (English, Russian, Spanish), classification, inflection, phonetics, tfidf, WordNet, jaro-winkler, Levenshtein distance, Dice's Coefficient) facilities for node.
The npm package melchior-natural receives a total of 0 weekly downloads. As such, melchior-natural popularity was classified as not popular.
We found that melchior-natural 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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