wink-nlp - npm Package Compare versions

Comparing version 1.12.2 to 1.12.3

.nyc_output/processinfo/index.json

@@ -1,1 +0,1 @@
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CHANGELOG.md

@@ -0,1 +1,9 @@
+# [Operational update](https://github.com/winkjs/wink-nlp/releases/tag/1.12.3)
+## Version 1.12.3 November 18, 2022
+
+### ⚙️ Updates
+- README is now more informative and links to examples and benchmarks 👍
+- Benchmarked on latest machine, browser versions 🖥
+
+
 # [Ready for Node.js version 18](https://github.com/winkjs/wink-nlp/releases/tag/1.12.2)
@@ -2,0 +10,0 @@ ## Version 1.12.2 October 13, 2022

package.json

 {
   "name": "wink-nlp",
-  "version": "1.12.2",
+  "version": "1.12.3",
   "description": "Developer friendly Natural Language Processing ✨",
@@ -49,8 +49,8 @@ "keywords": [
     "benchmark": "^2.1.4",
-    "chai": "^4.3.4",
+    "chai": "^4.3.6",
     "coveralls": "^3.1.1",
     "docker": "^1.0.0",
-    "dtslint": "^4.1.3",
-    "eslint": "^7.32.0",
-    "mocha": "^9.0.3",
+    "dtslint": "^4.2.1",
+    "eslint": "^8.25.0",
+    "mocha": "^10.0.0",
     "nyc": "^15.1.0"
@@ -57,0 +57,0 @@ },

README.md

@@ -8,40 +8,43 @@ # winkNLP
 
-winkNLP is a JavaScript library for Natural Language Processing (NLP). Designed specifically to make development of NLP solutions **easier** and **faster**, winkNLP is optimized for the right balance of performance and accuracy.  The package can handle large amount of raw text at speeds over **525,000 tokens/second**. And with a test coverage of ~100%, winkNLP is a tool for building production grade systems with confidence.
+WinkNLP is a JavaScript library for Natural Language Processing (NLP). Designed specifically to make development of NLP applications **easier** and **faster**, winkNLP is optimized for the right balance of performance and accuracy. 
 
-[<img src="https://user-images.githubusercontent.com/9491/100614781-ad17bb00-333c-11eb-87ab-2ae41aa21285.png" alt="Wink Wizard Showcase">](https://winkjs.org/showcase-wiz/)
+It is built ground up with a lean code base that has [no external dependency](https://snyk.io/test/github/winkjs/wink-nlp?tab=dependencies). A test coverage of [~100%](https://coveralls.io/github/winkjs/wink-nlp?branch=master) and compliance with the [Open Source Security Foundation best practices](https://bestpractices.coreinfrastructure.org/en/projects/6035) make winkNLP the ideal tool for building production grade systems with confidence.
 
+WinkNLP with full [Typescript support](https://github.com/winkjs/wink-nlp/blob/master/types/index.d.ts), runs on Node.js and browsers.
 
-## Features
-WinkNLP has a comprehensive natural language processing (NLP) pipeline covering tokenization, sentence boundary detection (sbd), negation handling, sentiment analysis, part-of-speech (pos) tagging, named entity recognition (ner), custom entities recognition (cer):
+## Build amazing apps quickly
+| [Wikipedia article timeline](https://winkjs.org/showcase-timeline/) | [Context aware word cloud](https://observablehq.com/@winkjs/how-to-create-a-context-aware-word-cloud) | [Key sentences detection](https://observablehq.com/@winkjs/how-to-visualize-key-sentences-in-a-document) |
+| --- | --- | --- |
+| [<img src="https://user-images.githubusercontent.com/29990/202497363-19c30578-8146-4f36-9c4b-4de613610837.png">](https://winkjs.org/showcase-timeline/)| [<img src="https://user-images.githubusercontent.com/29990/202506181-1a926ee0-788f-4aa1-aeac-a097f09fe747.png">](https://observablehq.com/@winkjs/how-to-create-a-context-aware-word-cloud)|[<img src="https://user-images.githubusercontent.com/29990/202506490-7f999d12-8319-4969-b92b-0649559ffbe6.png">](https://observablehq.com/@winkjs/how-to-visualize-key-sentences-in-a-document)|
 
-<img src="https://winkjs.org/images/wink-nlp-processing-pipeline.png" alt="Processing pipeline: text, tokenization, SBD, negation, sentiment, NER, POS, CER" title="WinkNLP processing pipeline">
+Head to  [live examples](https://winkjs.org/examples.html) to explore further.
 
-At every stage a range of properties become accessible for tokens, sentences, and entities. Read more about the processing pipeline and how to configure it in the [winkNLP documentation](https://winkjs.org/wink-nlp/processing-pipeline.html).
+## Blazing fast
+WinkNLP can easily process large amount of raw text at speeds over <mark>**650,000 tokens/second**</mark> on a M1 Macbook Pro in both browser and Node.js environments. It even runs smoothly on a low-end smartphone's browser.
 
+| Environment | Benchmarking Command |
+|--- | --- |
+| Node.js | [node benchmark/run](https://github.com/winkjs/wink-nlp/tree/master/benchmark) |
+| Browser | [How to measure winkNLP's speed on browsers?](https://observablehq.com/@winkjs/how-to-measure-winknlps-speed-on-browsers) |
 
-It packs a rich feature set into a small foot print codebase of [under 1500 lines](https://coveralls.io/github/winkjs/wink-nlp?branch=master):
+## Features
+WinkNLP has a [comprehensive natural language processing (NLP) pipeline](https://winkjs.org/wink-nlp/processing-pipeline.html) covering tokenization, sentence boundary detection (sbd), negation handling, sentiment analysis, part-of-speech (pos) tagging, named entity recognition (ner), custom entities recognition (cer). It offers a rich feature set:
 
-1. Fast, lossless & multilingual [tokenizer](https://winkjs.org/wink-nlp/processing-pipeline.html)
+<table>
+<tr><td width="330px;">Fast, lossless & multilingual tokenizer ⚡️</td><td>For example, the text string <b><code style="font-size: 0.9em">"¡Hola! नमस्कार! Hi! Bonjour chéri"</code></b> tokenizes as <code style="font-size: 0.9em">["¡", "Hola", "!", "नमस्कार", "!", "Hi", "!", "Bonjour", "chéri"]</code>.  It tokenizes text at a speed close to <b>4 million</b> tokens/second on a M1 MBP's browser.</td></tr>
+<tr><td>Developer friendly and intuitive <a href="https://winkjs.org/wink-nlp/getting-started.html">API</a> 💚</td><td>As simple as DOM manipulation; most <a href="https://observablehq.com/@winkjs/how-to-build-a-naive-wikification-tool?collection=@winkjs/winknlp-recipes">live examples</a> have <b>30-40</b> lines of code.</td></tr>
+<tr><td>Best-in-class <a href="https://winkjs.org/wink-nlp/visualizing-markup.html">text visualization</a> 🖼</td><td>Programmatically <b><a href="https://winkjs.org/wink-nlp/markup.html">mark</a></b> tokens, sentences, entities, etc. using HTML mark or any other tag of your choice.</td></tr>
+<tr><td>Extensive text processing features ♻️</td><td>Checkout how a <a href="https://github.com/winkjs/wink-naive-bayes-text-classifier#readme">Naive Bayes classifier</a> achieves <b>impressive</b> chatbot intent classification accuracy with right kind of preprocessing with winkNLP.</td></tr>
+<tr><td>Pre-trained <a href="https://winkjs.org/wink-nlp/language-models.html">language models</a> 🔠</td><td>Compact sizes starting from <b>&lt;3MB</b>.</td></tr>
+<tr><td>Host of <a href="https://winkjs.org/wink-nlp/its-as-helper.html">utilities & tools</a> 💼</td><td>BM25 vectorizer; Several similarity methods – Cosine, Tversky, Sørensen-Dice, Otsuka-Ochiai; Helpers to get bag of words, frequency table, lemma/stem, stop word removal and many more.</td></tr>
+</table>
 
-2. Developer friendly and intuitive [API](https://winkjs.org/wink-nlp/getting-started.html)
+WinkJS also has packages like [Naive Bayes classifier](https://github.com/winkjs/wink-naive-bayes-text-classifier), [multi-class averaged perceptron](https://github.com/winkjs/wink-perceptron) and [popular token and string distance methods](https://github.com/winkjs/wink-distance), which complement winkNLP. 
 
-3. Built-in [API](https://winkjs.org/wink-nlp/visualizing-markup.html) to aid [text visualization](https://observablehq.com/@winkjs/how-to-perform-sentiment-analysis?collection=@winkjs/winknlp-recipes)
+## Documentation
+- [Concepts](https://winkjs.org/wink-nlp/getting-started.html) — everything you need to know to get started.
+- [API Reference](https://winkjs.org/wink-nlp/read-doc.html) — explains usage of APIs with examples.
+- [Change log](https://github.com/winkjs/wink-nlp/blob/master/CHANGELOG.md) — version history along with the details of breaking changes, if any.
+- [Examples](https://winkjs.org/examples.html) — live examples with code to give you a head start.
 
-4. Extensive [text processing features](https://winkjs.org/wink-nlp/its-as-helper.html) such as bag-of-words, frequency table, stop word removal, readability statistics computation and many more.
-
-5. Pre-trained [language models](https://winkjs.org/wink-nlp/language-models.html) with sizes starting from <3MB onwards
-
-6. [BM25-based vectorizer](https://winkjs.org/wink-nlp/bm25-vectorizer.html)
-
-7. Multiple [similarity](https://winkjs.org/wink-nlp/similarity.html) methods
-
-8. Word vector integration
-
-9. No external dependencies
-
-10. [Runs on web browsers](https://winkjs.org/wink-nlp/wink-nlp-in-browsers.html)
-
-11. [Typescript support](https://github.com/winkjs/wink-nlp/blob/master/types/index.d.ts).
-
-
 ## Installation
@@ -55,3 +58,3 @@
 
-In order to use winkNLP after its installation, you also need to install a language model according to the node version used. The following table outlines the version specific installation command:
+In order to use winkNLP after its installation, you also need to install a language model according to the node version used. The table below outlines the version specific installation command:
 
@@ -63,3 +66,3 @@ | Node.js Version |Installation |
 
-The [wink-eng-lite-web-model](https://github.com/winkjs/wink-eng-lite-web-model) is designed to work with Node.js version 16 or 18. It can also work on browsers as described in the next section.
+The [wink-eng-lite-web-model](https://github.com/winkjs/wink-eng-lite-web-model) is designed to work with Node.js version 16 or 18. It can also work on browsers as described in the next section. This is the **recommended** model.
 
@@ -71,60 +74,45 @@ The second command installs the [wink-eng-lite-model](https://github.com/winkjs/wink-eng-lite-model), which works with Node.js version 14 or 12.
 
-## Getting Started
-The "Hello World!" in winkNLP is given below:
+### Get started
+Here is the "Hello World!" of winkNLP:
 
 ```javascript
-// Load wink-nlp package  & helpers.
+// Load wink-nlp package.
 const winkNLP = require( 'wink-nlp' );
-// Load "its" helper to extract item properties.
-const its = require( 'wink-nlp/src/its.js' );
-// Load "as" reducer helper to reduce a collection.
-const as = require( 'wink-nlp/src/as.js' );
-// Load english language model — light version.
-const model = require( 'wink-eng-lite-model' );
+// Load english language model.
+const model = require( 'wink-eng-lite-web-model' );
 // Instantiate winkNLP.
 const nlp = winkNLP( model );
-
+// Obtain "its" helper to extract item properties.
+const its = nlp.its;
+// Obtain "as" reducer helper to reduce a collection.
+const as = nlp.as;
+ 
 // NLP Code.
 const text = 'Hello   World🌎! How are you?';
 const doc = nlp.readDoc( text );
-
+ 
 console.log( doc.out() );
 // -> Hello   World🌎! How are you?
-
+ 
 console.log( doc.sentences().out() );
 // -> [ 'Hello   World🌎!', 'How are you?' ]
-
+ 
 console.log( doc.entities().out( its.detail ) );
 // -> [ { value: '🌎', type: 'EMOJI' } ]
-
+ 
 console.log( doc.tokens().out() );
 // -> [ 'Hello', 'World', '🌎', '!', 'How', 'are', 'you', '?' ]
-
+ 
 console.log( doc.tokens().out( its.type, as.freqTable ) );
 // -> [ [ 'word', 5 ], [ 'punctuation', 2 ], [ 'emoji', 1 ] ]
 ```
-Experiment with the above code on [RunKit](https://npm.runkit.com/wink-nlp).
+Experiment with winkNLP on [RunKit](https://npm.runkit.com/wink-nlp).
 
-### Explore Further
-Dive into [winkNLP's concepts](https://winkjs.org/wink-nlp/getting-started.html)  or head to **[winkNLP recipes](https://observablehq.com/collection/@winkjs/winknlp-recipes)** for common NLP tasks or just explore live [showcases](https://winkjs.org/showcase.html) to learn:
-
-#### [Wikipedia Timeline](https://winkjs.org/showcase-timeline/) ⏳
-Reads any wikipedia article and generates a visual timeline of all its events.
-
-#### [NLP Wizard](https://winkjs.org/showcase-wiz/) 🧙
-Performs tokenization, sentence boundary detection, pos tagging, named entity detection and sentiment analysis of user input text in real time.
-
-#### [Naive Wikification Tool](https://observablehq.com/@winkjs/how-to-build-a-naive-wikification-tool) 🔗
-Links entities such as famous persons, locations or objects to the relevant Wikipedia pages.
-
-
 ## Speed & Accuracy
-The [winkNLP](https://winkjs.org/wink-nlp/) processes raw text at **~525,000 tokens per second** with its default language model — [wink-eng-lite-model](https://github.com/winkjs/wink-eng-lite-model), when [benchmarked](https://github.com/bestiejs/benchmark.js) using "Ch 13 of Ulysses by James Joyce" on a 2.2 GHz Intel Core i7 machine with 16GB RAM. The processing included the entire NLP pipeline — tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech tagging, and named entity extraction. This speed is way ahead of the prevailing speed benchmarks.
+The [winkNLP](https://winkjs.org/wink-nlp/) processes raw text at **~650,000 tokens per second** with its [wink-eng-lite-web-model](https://github.com/winkjs/wink-eng-lite-web-model), when [benchmarked](https://github.com/bestiejs/benchmark.js) using "Ch 13 of Ulysses by James Joyce" on a M1 Macbook Pro machine with 16GB RAM. The processing included the entire NLP pipeline — tokenization, sentence boundary detection, negation handling, sentiment analysis, part-of-speech tagging, and named entity extraction. This speed is way ahead of the prevailing speed benchmarks.
 
-The benchmark was conducted on [Node.js versions 14.8.0, and 12.18.3](https://nodejs.org/en/about/releases/). It delivered similar/better performance on Node.js versions 16/18.
+The benchmark was conducted on [Node.js versions 16, and 18](https://nodejs.org/en/about/releases/).
 
-The [winkNLP](https://winkjs.org/wink-nlp/) delivers similar performance on browsers; its performance on a specific machine/browser combination can be measured using the Observable notebook — [How to measure winkNLP's speed on browsers?](https://observablehq.com/@winkjs/how-to-measure-winknlps-speed-on-browsers?collection=@winkjs/winknlp-recipes).
+It pos tags a subset of WSJ corpus with an accuracy of **~94.7%** — this includes *tokenization of raw text prior to pos tagging*. The present state-of-the-art is at ~97% accuracy but at lower speeds and is generally computed using gold standard pre-tokenized corpus.
 
-It pos tags a subset of WSJ corpus with an accuracy of **~94.7%** — this includes *tokenization of raw text prior to pos tagging*. The current state-of-the-art is at ~97% accuracy but at lower speeds and is generally computed using gold standard pre-tokenized corpus.
-
 Its general purpose sentiment analysis delivers a [f-score](https://en.wikipedia.org/wiki/F1_score) of **~84.5%**, when validated using Amazon Product Review [Sentiment Labelled Sentences Data Set](https://archive.ics.uci.edu/ml/machine-learning-databases/00331/) at [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). The current benchmark accuracy for **specifically trained** models can range around 95%.
@@ -135,7 +123,2 @@
 
-## Documentation
-- [Concepts](https://winkjs.org/wink-nlp/getting-started.html) — everything you need to know to get started.
-- [API Reference](https://winkjs.org/wink-nlp/read-doc.html) — explains usage of APIs with examples.
-- [Change log](https://github.com/winkjs/wink-nlp/blob/master/CHANGELOG.md) — version history along with the details of breaking changes, if any.
-- [Showcases](https://winkjs.org/showcase.html) — live examples with code to give you a head start.
 
@@ -154,4 +137,4 @@ ## Need Help?
 
-## About wink
-[Wink](https://winkjs.org/) is a family of open source packages for **Natural Language Processing**, **Machine Learning**, and **Statistical Analysis** in NodeJS. The code is **thoroughly documented** for easy human comprehension and has a **test coverage of ~100%** for reliability to build production grade solutions.
+## About winkJS
+[WinkJS](https://winkjs.org/) is a family of open source packages for **Natural Language Processing**, **Machine Learning**, and **Statistical Analysis** in NodeJS. The code is **thoroughly documented** for easy human comprehension and has a **test coverage of ~100%** for reliability to build production grade solutions.
 
@@ -162,2 +145,2 @@ ## Copyright & License
 
-It is licensed under the terms of the MIT License.
+It is licensed under the terms of the MIT License.

runkit-example.js

@@ -0,20 +1,29 @@
+// Load wink-nlp package.
 const winkNLP = require( 'wink-nlp' );
-const its = require( 'wink-nlp/src/its.js' );
-// Use web model for RunKit.
+// Load english language model — light version.
 const model = require( 'wink-eng-lite-web-model' );
+// Instantiate winkNLP.
 const nlp = winkNLP( model );
+// Obtain "its" helper to extract item properties.
+const its = nlp.its;
+// Obtain "as" reducer helper to reduce a collection.
+const as = nlp.as;
 
-const text = 'Its quarterly profits jumped 76% to $1.13 billion for the three months to December, from $639million of previous year.';
+// NLP Code.
+const text = 'Hello   World🌎! How are you?';
 const doc = nlp.readDoc( text );
-// Print tokens.
+
+console.log( doc.out() );
+// -> Hello   World🌎! How are you?
+
+console.log( doc.sentences().out() );
+// -> [ 'Hello   World🌎!', 'How are you?' ]
+
+console.log( doc.entities().out( its.detail ) );
+// -> [ { value: '🌎', type: 'EMOJI' } ]
+
 console.log( doc.tokens().out() );
-// Print each token's type.
-console.log( doc.tokens().out( its.type ) );
-// Print details of each entity.
-console.log( doc.entities().out( its.detail ) );
-// Markup entities along with their type for highlighting them in the text.
-doc.entities().each( ( e ) => {
-  e.markup( '<mark>', `<sub style="font-weight:900"> ${e.out(its.type)}</sub></mark>` );
-} );
-// Render them as HTML via RunKit
-doc.out( its.markedUpText );
+// -> [ 'Hello', 'World', '🌎', '!', 'How', 'are', 'you', '?' ]
+
+console.log( doc.tokens().out( its.type, as.freqTable ) );
+// -> [ [ 'word', 5 ], [ 'punctuation', 2 ], [ 'emoji', 1 ] ]

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