Overview of DropSuit NLP and the dnkscr Function
DropSuit NLP is an open-source JavaScript and Node.js library offering diverse functions for natural language processing and data manipulation. The dnkscr function is one of its modules, designed for extracting derivative nouns from a given input string or json object and returns the count of scores for each root key. It is available under the Apache License 2.0.
The dnkscr function is a part of the DropSuit NLP library, it's a JavaScript and Node.js function that extracts derivative nouns from a given input string or json object and returns the count of scores for each root key. It's open-source and available under the Apache License 2.0.
Add the library function by installing it via npm:
npm install dropsuit-dnkscr
Import the library in your project:
const dropsuit_dnkscr = require("dropsuit-dnkscr");
Process intents.json using 'jsonIntStrct' function:
const json_int = require("dropsuit-dnkscr/jsobj.js");
let intentData = json_int.jsonIntStrct("assets/json/intents.json");
Process rootkeyval.json using 'jsonRootKeyStrct' function:
const json_rk = require("dropsuit-dnkscr/jsobjrk.js");
let rootkeysData = json_rk.jsonRootKeyStrct("assets/json/root_keys.json");
Set 'stopWordsAmount', a number 0-2, where 0 is minimum, 1 adds extra, and 2 is maximum from 3 lists.
Set boolean parameter (true/false) argument value to display console log processing results output information in terminal:
let dsdnkscr = new dropsuit_dnkscr(null, intentData.req_arr, intentData.res_arr, rootkeysData, stopWordsAmount, false);
- input: A string input to process, or keep NULL to process the constructor 'input'. Processes the default object instance json key value (req_arr: requests), (res_arr: responses) array patterns.
- depth: Derivative noun search level depth parameter arguments (1-3).
- proctype: Specifies the type of processing to be done, either 0 or 1. A value of 0 means that repeated keys will not be counted, while a value of 1 allows repeated keys to be counted.
- score() Returns a count of the direct or derivative noun root keys inference score.
The algorithm is a multi-dimensional sentiment analysis method that evaluates the meaning of words or sentences by determining the score of derivative nouns within the data structure. The process involves extracting nouns from the input phrase, searching for sentences that include those nouns, and repeating this process to expand the search according to the specified depth level.
The algorithm uses a dichotomous model based on the Porphyrian tree to assign semantic weights to the nouns and interpret their meaning, with the values derived from the downward curve principle and considering both oppositions preponderance and equality contradiction in the output. The default depth level is set to 1, but the function is limited to a maximum of 3 due to the computational complexity of processing large amounts of data (word/sentence -> nouns -> sentences -> nouns -> sentences -> nouns -> sentences).
┌──────────────────────┐
│ Input Sentence Words
└──┬───────────────────┘
┌──┴────────────┐
│ Extract Nouns
└──┬────────────┘
┌──┴───────────────────┐
│ Derivative Sentences
└──┬───────────────────┘
┌──┴─────────────────────┐
│ Derivative Sent. Nouns
└──┬─────────────────────┘
┌──┴────────────────┐
│ Compare with Keys
└──┬────────────────┘
┌──┴────────────────────────┐
│ If equal incr. Root Value
└──┬────────────────────────┘
┌──┴────────────────────────┐
│ Count & Weigh Oppositions
└──┬────────────────────────┘
┌──┴───────────────────────┐
│ Score highest Peak Value
└──┬───────────────────────┘
┌──┴─────────────┐
│ Downward Curve
└────────────────┘
The 'dnkscr' and 'dnkscr_async' functions provide the capability to process requests in both synchronous and asynchronous modes. For more detailed information on how to use the functions in either mode, please refer to the usage example
let out = dsdnkscr.dnkscr(myInputString, 3, 2);
console.log(out);
Return object instance:
Input:
[ 'computer' ]
nDerivative sentences nouns:
[
'language new programmers computer eating habits balanced avoid junk food take breaks fruits nuts seeds carrots celery delivery shipping receive definition explain mean tech mimics perception reasoning learning technology acts simulates act humans robots automated specific used variety including manufacturing exploration scientific research child young age majority behaves thinks algorithms anyone available chat items simple terms bye farewell hope see wide range books state art sensor tablets best beginner easiest programming beginners starting write programs python java javascript good choices people recommend often considered'
]
Derivative Nouns (Depth Level: 1 ) length: ( 58 ): [
'recommended', 'healthy', 'snacks', 'users',
'focus', 'diet', 'working', 'long',
'hours', 'components', 'computers', 'work',
'artificial', 'intelligence', 'develops', 'systems',
'perform', 'tasks', 'requiring', 'human',
'speech', 'recognition', 'decision', 'making',
'ai', 'science', 'focused', 'creating',
'machines', 'intelligent', 'behavior', 'robot',
'machine', 'designed', 'carry', 'complex',
'series', 'actions', 'automatically', 'especially',
'programmed', 'electronic', 'device', 'processes',
'data', 'cpu', 'ram', 'storage',
'motherboard', 'power', 'supply', 'processing',
'using', 'central', 'unit', 'storing',
'processed', 'memory'
]
Score count: [
1, 0, 0, 0, 0,
0, 1, 2, 1, 2,
4, 0
]
POS/NEG
Positive: ( 1 ) Negative: ( 0 )
MAT/PAT
Maternal: ( 0 ) Paternal: ( 0 )
PORPHYRIAN TREE
Material: ( 0 ) Immaterial: ( 0 )
Animate: ( 1 ) Inanimate: ( 2 )
Sensitive: ( 1 ) Insensitive: ( 2 )
Rational: ( 4 ) Irrational: ( 0 )
Output:
- Score Downward Curve and Oppositions Equality Contradiction -
{
level_depth: 1,
score_ptDcoec: [
'4 -> rational',
'2 -> inanimate',
'2 -> insensitive',
'1 -> positive'
],
score: [Function: score]
}
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