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wink-ner - npm Package Compare versions
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| { | ||
| "name": "wink-ner", | ||
| "version": "0.2.0", | ||
| "version": "0.2.1", | ||
| "description": "Language agnostic named entity recognizer", | ||
@@ -5,0 +5,0 @@ "keywords": [ |
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@@ -19,2 +19,38 @@ # wink-ner | ||
| ```javascript | ||
| // Load wink ner. | ||
| var ner = require( 'wink-ner' ); | ||
| // Create your instance of wink ner & use defualt config. | ||
| var myNER = ner(); | ||
| // Define training data. | ||
| var trainingData = [ | ||
| { text: 'manchester united', entityType: 'club', uid: 'manu' }, | ||
| { text: 'manchester', entityType: 'city' }, | ||
| { text: 'U K', entityType: 'country', uid: 'uk' } | ||
| ]; | ||
| // Learn from the training data. | ||
| myNER.learn( trainingData ); | ||
| // Since recognize() requires tokens, use wink-tokenizer. | ||
| var winkTokenizer = require( 'wink-tokenizer' ); | ||
| // Instantiate it and extract tokenize() api. | ||
| var tokenize = winkTokenizer().tokenize; | ||
| // Tokenize the sentence. | ||
| var tokens = tokenize( 'Manchester United is a football club based in Manchester, U. K.' ) | ||
| // Simply Detect entities! | ||
| myNER.recognize( tokens ); | ||
| // -> [ | ||
| // { entityType: 'club', uid: 'manu', originalSeq: [ 'Manchester', 'United' ], value: 'manchester united', tag: 'word' }, | ||
| // { value: 'is', tag: 'word' }, | ||
| // { value: 'a', tag: 'word' }, | ||
| // { value: 'football', tag: 'word' }, | ||
| // { value: 'club', tag: 'word' }, | ||
| // { value: 'based', tag: 'word' }, | ||
| // { value: 'in', tag: 'word' }, | ||
| // { entityType: 'city', value: 'Manchester', tag: 'word', originalSeq: [ 'Manchester' ], uid: 'manchester' }, | ||
| // { value: ',', tag: 'punctuation' }, | ||
| // { entityType: 'country', uid: 'uk', originalSeq: [ 'U', '.', 'K' ], value: 'u k', tag: 'word' }, | ||
| // { value: '.', tag: 'punctuation' } | ||
| // ] | ||
| ``` | ||
| ### Documentation | ||
@@ -21,0 +57,0 @@ Check out the [named entity recognizer API documentation](http://winkjs.org/wink-ner/) to learn more. |
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@@ -36,3 +36,3 @@ // wink-ner | ||
| * // Load wink ner. | ||
| * var tagger = require( 'wink-ner' ); | ||
| * var ner = require( 'wink-ner' ); | ||
| * // Create your instance of wink ner. | ||
@@ -277,6 +277,6 @@ * var myNER = ner(); | ||
| * | ||
| * It can be use to learn or update learnings incrementally; but it can not be | ||
| * It can be used to learn or update learnings incrementally; but it can not be | ||
| * used to unlearn or delete one or more entities. | ||
| * | ||
| * If duplicated entity definitions are enountered then all the entries except | ||
| * If duplicate entity definitions are enountered then all the entries except | ||
| * the **last one** are ignored. | ||
@@ -286,3 +286,3 @@ * | ||
| * should be added as `'u s a'` — this ensure correct detection of | ||
| * `U S A` or `U. S. A.` or `U.S.A.` as `USA.` | ||
| * `U S A` or `U. S. A.` or `U.S.A.` as `USA` \[Refer to the example below\]. | ||
| * | ||
@@ -294,3 +294,3 @@ * @param {object[]} entities — where each element defines an entity via | ||
| * | ||
| * In addition to these two properties, you may optionally defined two more | ||
| * In addition to these two properties, you may optionally define two more | ||
| * properties as described in the table below. Apart from these **4 properties**, | ||
@@ -317,7 +317,7 @@ * if any additional property is defined, the same is copied to the output | ||
| * var trainingData = [ | ||
| * { text: 'manchester united', entityType: 'club' }, | ||
| * { text: 'manchester united', entityType: 'club', uid: 'manu' }, | ||
| * { text: 'manchester', entityType: 'city' }, | ||
| * { text: 'uk', entityType: 'country' } | ||
| * { text: 'U K', entityType: 'country', uid: 'uk' } | ||
| * ]; | ||
| * learn( trainingData ); | ||
| * myNER.learn( trainingData ); | ||
| * // -> 3 | ||
@@ -461,4 +461,25 @@ */ | ||
| * | ||
| * @return {object[]} of updated `tokens.` | ||
| * @return {object[]} of updated `tokens` with entities tagged. | ||
| * @example | ||
| * // Use wink tokenizer. | ||
| * var winkTokenizer = require( 'wink-tokenizer' ); | ||
| * // Instantiate it and use tokenize() api. | ||
| * var tokenize = winkTokenizer().tokenize; | ||
| * var tokens = tokenize( 'Manchester United is a professional football club based in Manchester, U. K.' ) | ||
| * // Detect entities. | ||
| * myNER.recognize( tokens ); | ||
| * // -> [ | ||
| * // { entityType: 'club', uid: 'manu', originalSeq: [ 'Manchester', 'United' ], value: 'manchester united', tag: 'word' }, | ||
| * // { value: 'is', tag: 'word' }, | ||
| * // { value: 'a', tag: 'word' }, | ||
| * // { value: 'professional', tag: 'word' }, | ||
| * // { value: 'football', tag: 'word' }, | ||
| * // { value: 'club', tag: 'word' }, | ||
| * // { value: 'based', tag: 'word' }, | ||
| * // { value: 'in', tag: 'word' }, | ||
| * // { value: 'Manchester', tag: 'word', originalSeq: [ 'Manchester' ], uid: 'manchester', entityType: 'city' }, | ||
| * // { value: ',', tag: 'punctuation' }, | ||
| * // { entityType: 'country', uid: 'uk', originalSeq: [ 'U', '.', 'K' ], value: 'u k', tag: 'word' }, | ||
| * // { value: '.', tag: 'punctuation' } | ||
| * // ] | ||
| */ | ||
@@ -465,0 +486,0 @@ var recognize = function ( tokens ) { |
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Fixed alerts
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Improved metrics
- Total package byte prevSize
60205
4.7%- Lines of code
547
3.99%- Number of lines in readme file
68
112.5%No dependency changes