Vocabulary 0.0.3

Table of Contents

• What is it?

• Features

• Why should I use Vocabulary

  • Wordnet Comparison

• Installation

  • pip

  • from source

• Usage

  • Demo

  • Help

• How does it Work

• Contributing

  • To Do

  • Tests

• Bugs

• License

What is it

For a given word, using Vocabulary, you can get it’s

• Meaning

  • Synonyms

  • Antonyms

• Part of speech : whether the word is a noun, interjection or an adverb et el

• Usage example : a quick example on how to use the word in a sentence

• Pronuciation

• Hyphenation : shows the particular stress points(if any)

Features

• Written in uncomplicated Python

• Returns JSON objects

• Minimum dependencies ( just uses requests (https://github.com/kennethreitz/requests))

• Easy to install

• A decent substitute to Wordnet(well almost!) Wanna see? Here is a small comparison

• Stupidly easy to use

• Fast!

• Supports

  • both, python2.* and python3.*

  • Works on Mac, Linux and Windows

Why should I use Vocabulary

Wordnet is a great resource. No doubt about it! So why should you use Vocabulary when we already have Wordnet out there?

My 2 cents

>>> from nltk.corpus import wordnet
>>> syns = wordnet.synsets('car')
>>> syns[0].lemmas[0].name
'car'
>>> [s.lemmas[0].name for s in syns]
['car', 'car', 'car', 'car', 'cable_car']

>>> [l.name for s in syns for l in s.lemmas]
['car', 'auto', 'automobile', 'machine', 'motorcar', 'car', 'railcar', 'railway_car', 'railroad_car', 'car', 'gondola', 'car', 'elevator_car', 'cable_car', 'car']

>>> from vocabulary import Vocabulary as vb
>>> vb.synonym("car")
'[{"seq": 0, "text": "automotive"}, {"seq": 1, "text": "motor"}, {"seq": 2, "text": "wagon"}, {"seq": 3, "text": "cart"}, {"seq": 4, "text": "automobile"}]'
>>>

Installation

$ git clone https://github.com/prodicus/vocabulary.git
$ cd vocabulary/
$ pip install -r requirements.txt
$ python setup.py install

Demo

Usage

A Simple demonstration of the module

## Importing the module
>>> from vocabulary import Vocabulary as vb

## Extracting "Meaning"
>>> vb.meaning("hillbilly")
'[{"text": "Someone who is from the hills; especially from a rural area, with a connotation of a lack of refinement or sophistication.", "seq": 0}, {"text": "someone who is from the hills", "seq": 1}, {"text": "A white person from the rural southern part of the United States.", "seq": 2}]'
>>>

## "Synonym"
>>> vb.synonym("hurricane")
'[{"text": "storm", "seq": 0}, {"text": "tropical cyclone", "seq": 1}, {"text": "typhoon", "seq": 2}, {"text": "gale", "seq": 3}]'
>>>

## "Antonym"
>>> vb.antonym("respect")
'{"text": ["disesteem", "disrespect"]}'
>>> vb.antonym("insane")
'{"text": ["sane"]}'

## "Part of Speech"
>>> vb.part_of_speech("hello")
'[{"text": "interjection", "example:": "Used to greet someone, answer the telephone, or express surprise.", "seq": 0}]'
>>>

## "Usage Examples"
>>> vb.usage_example("chicanery")
'[{"text": "The Bush Administration is now the commander-in-theif (lower-case intentional) thanks to their chicanery.", "seq": 0}]'
>>>

## "Pronunciation"
>>> vb.pronunciation("hippopotamus")
[{'raw': '(hĭpˌə-pŏtˈə-məs)', 'rawType': 'ahd-legacy', 'seq': 0}, {'raw': 'HH IH2 P AH0 P AA1 T AH0 M AH0 S', 'rawType': 'arpabet', 'seq': 0}]
>>>

## "Hyphenation"
>>> vb.hyphenation("hippopotamus")
'[{"text": "hip", "type": "secondary stress", "seq": 0}, {"text": "po", "seq": 1}, {"text": "pot", "type": "stress", "seq": 2}, {"text": "a", "seq": 3}, {"text": "mus", "seq": 4}]'
>>> vb.hyphenation("amazing")
'[{"text": "a", "seq": 0}, {"text": "maz", "type": "stress", "seq": 1}, {"text": "ing", "seq": 2}]'
>>>

Help

If you need to see the usage for any of the methods, do a

>>> from vocabulary import Vocabulary as vb
>>> help(vb.meaning)
Help on function meaning in module vocabulary.vocabulary:

meaning(phrase, source_lang='en', dest_lang='en')
    make calls to the
    - glosbe API(default choice)
    - Wordnik API

    Wordnik's API gives less results so not Using it here for getting the meanings

    params:
    =======
    source_lang, dest_lang (both default to "en" if nothing is specified)

    Usage:
    ======
    >>> from vocabulary import Vocabulary as vb
    >>> vb.meaning("levitate")
    '[{"text": "(intransitive) Be suspended in the air, as if in defiance of gravity.", "seq": 0}, {"text": "(transitive) To cause to rise in the air and float, as if in defiance of gravity.", "seq": 1}]'
    >>>
(END)

and so on for other functions

How does it work

Under the hood, it makes use of 4 awesome API’s to give you consistent results. The API’s being

• Urbandict

• Glosbe

• BighugeLabs

• Wordnik

Contributing

Feel free to contribute

1. Fork it.

2. Create your feature branch (git checkout -b my-new-awesome-feature)

3. Commit your changes (git commit -am 'Added <xyz> feature')

4. Push to the branch (git push origin my-new-awesome-feature)

5. Create new Pull Request

To do

• Add translate module

Tests

Vocabulary uses unittesting for testing.

Run the test cases by doing a

$ ./tests.py -v
test_antonym_1 (__main__.TestModule) ... ok
test_antonym_2 (__main__.TestModule) ... ok
test_hyphenation (__main__.TestModule) ... ok
test_meaning (__main__.TestModule) ... ok
test_partOfSpeech_1 (__main__.TestModule) ... ok
test_partOfSpeech_2 (__main__.TestModule) ... ok
test_pronunciation (__main__.TestModule) ... ok
test_synonym (__main__.TestModule) ... ok
test_usageExamples (__main__.TestModule) ... ok

----------------------------------------------------------------------
Ran 9 tests in 7.742s

OK
(testvocab)

Bugs

Please report the bugs at the issue tracker

License :

MIT License © Tasdik Rahman

You can find a copy of the License at http://prodicus.mit-license.org/