En-parse NPM | npm.io

English Parse

English language syntactic dependency parser.

important This project is in a very early stage, it's rather an experiment than a real a project. Using it in production may hurt your feelings.

Installation and Usage

npm i --save en-parse

const parser = require("en-parse").default;
let parsed = parser(["PRP","VBP","TO","VBP"],["i","like","to","parse"]);

NOTE: tokens should be normalized (contractions, abbreviations and such all resolved to it's normal form).

The parsed variable is now an array like this one:

[
  {
    "label": "MWE",
    "type": "NP",
    "master": 1
  },
  {
    "label": "NSUBJ",
    "type": "NP",
    "master": 2
  },
  {
    "label": "ROOT",
    "type": "VP",
    "master": -1
  },
  {
    "label": "COMPMARK",
    "type": "PP",
    "master": 5
  },
  {
    "label": "NSUBJ",
    "type": "NP",
    "master": 5
  },
  {
    "label": "XCOMP",
    "type": "VP",
    "master": 2
  },
  {
    "label": "ADVMOD",
    "type": "ADV",
    "master": 8
  },
  {
    "label": "AUX",
    "type": "VBN",
    "master": 8
  },
  {
    "label": "ADVCL",
    "type": "VP",
    "master": 5
  },
  {
    "label": "PUNCT",
    "type": "PUNCT",
    "master": 2
  }
]

Annotation Specs:

Core Nominal Arguments

Annotation	Name	Example
`NSUBJ`	Nominal Subject	`I` like you
`DOBJ`	Direct Object	I like `you`
`IOBJ`	Indirect Object	she gave me `a book`
`NSUBJPASS`	Nominal Subject (passive)	`I` have been given a chance
`CCOMP`	Clausal Complement	The boss ordered to `dig` the hole
`XCOMP`	Open Clausal Complement	The boss told us to `dig`
`OBL`	Oblique Object	Give the toys to `the children`
`EXPL`	Expletive	I have been `there`
`ADVCL`	Adverb Clause Modifier	We were walking as the rain was `falling`
`ADVMOD`	Adverbial modifier	I don't eat `genetically` modified food.
`DISCOURSE`	Discourse	I like that `:)`
`VPRT`	Verbal Particle	I switched the TV `off`
`AUX`	Auxiliary	I am `going` to buy a new computer
`AUXPASS`	Auxiliary (passive)	I `have` been marked
`COMPMARK`	Complement Marker	I will do it `if` I like it
`ADVMARK`	Adverb Marker	I did it `after` I was convinced
`NOMD`	Nominal Modifier	`MSNBC` news had little effect on the market
`NUMMOD`	Numeric Modifier	I ate `2` eggs this morning
`ACL`	Clausal Modifier of a Noun	I saw the man you `love`
`AMOD`	Adjectival Modifier	you were `good` to him
`DET`	Determiner	`which` book you prefer?
`CASE`	Case Marking	I went `to` Rachel
`PUNCT`	Punctuation	Guys`,` keep calm`!`
`CC`	Coordinating Conjunction	Mathew `and` Alex
`INTERJ`	Interjection	Pass me the sugar, `please`
`EXT`	Extension	My name is `Alex` Corvi
`DEP`	Unrecognized dependency	how `what`

Why?

Current solutions uses statistical machine learning models which makes it 5 times slower (if not worse) than a rule based implementation like this one.
This is a javascript implementation, so it can be used for an in-browser natural language processing.
Also because it's written in javascript it might be preferred in smart bots to reduce the complexity of having your bot written in node.js and the natural language processor written in Python or C++.