quranic-nlp 1.1.8

quarnic nlp

QuaranicTools: A Python NLP Library for Quranic NLP

Language Processing and Digital Humanities Lab (Language.ML)

Part of Speech Tagging | Dependency Parsing | Lemmatizer | Multilingual Search
| Quranic Extractions | Revelation Order |
Embeddings (coming soon) | Translations

Quranic NLP

Quranic NLP is a computational toolbox to conduct various syntactic and semantic analyses of Quranic verses. The aim is to put together all available resources contributing to a better understanding/analysis of the Quran for everyone.

Contents:

• Installation
• Pipline (dep,pos,lem,root)
• Example
• Format inputs

Installation

To get started using Quranic NLP in your python project, you may simply install it via the pip package.

Install with pip

pip install quranic-nlp

You can check the requirements.txt file to see the required packages.

Pipeline

The NLP pipeline contains morphological information e.g., Lemmatizer as well as POS Tagger and Dependancy Parser in a Spacy-like pipeline.

from quranic_nlp import language

translation_translator = 'fa#1'
pips = 'dep,pos,root,lemma'
nlp = language.Pipeline(pips, translation_translator)

Doc object has different extensions. First, there are sentences in doc referring to the verses. Second, there are ayah in doc which is indicate number ayeh in soure. Third, there are surah in doc which is indicate name of soure. Fourth, there are revelation_order in doc which is indicate order of revelation of the ayeh. doc which is the list of Token also has its own extensions. The pips is information to use from quranic_nlp. The translation_translator is language for translate quran such that language (fa) or language along with # along with number books. For see all translate run below code

from quranic_nlp import utils
utils.print_all_translations()

Quranic NLP has its own spacy extensions. If related pipeline is not called, that extension cannot be used.

Format Inputs

There are three ways to format the input. First, number surah along with # along with number ayah. Second, name surah along with # along with number ayah. Third, search text in quran.

Note The last two calls require access to the net for an API call.

from quranic_nlp import language

translation_translator = 'fa#1'
pips = 'dep,pos,root,lemma'
nlp = language.Pipeline(pips, translation_translator)

doc = nlp('1#1')
doc = nlp('حمد#1')
doc = nlp('رب العالمین')

Example

from quranic_nlp import language

translation_translator = 'fa#1'
pips = 'dep,pos,root,lemma'
nlp = language.Pipeline(pips, translation_translator)

doc = nlp('1#4')

print(doc)
print(doc._.text)
print(doc._.surah)
print(doc._.ayah)
print(doc._.revelation_order)
print(doc._.sim_ayahs)
print(doc._.translations)

إِيَّاكَ نَعْبُدُ وَ إِيَّاكَ نَسْتَعِينُ نحن نحن
إِيَّاكَ نَعْبُدُ وَ إِيَّاكَ نَسْتَعِينُ 
فاتحه
4
63
['82#15', '83#11', '70#26', '51#12', '56#56', '82#17', '74#46', '37#20', '82#18', '15#35', '38#78', '26#82', '109#6', '51#6', '82#9', '107#1', '95#7', '40#16', '19#15', '19#33', '61#9', '9#33', '48#28', '21#103', '6#73', '3#26', '98#5', '83#5', '39#11', '40#14', '77#12', '50#42', '77#35', '77#13', '39#2', '36#71', '74#9', '85#2', '16#52', '30#30', '42#13', '75#1', '30#43', '75#6', '40#29', '39#14', '43#77', '5#3', '86#9', '26#189', '40#65', '26#87', '38#81', '15#38', '7#51', '23#113', '23#16', '79#6', '51#13', '77#14', '37#26', '9#11', '3#24', '114#2', '82#19', '11#103', '34#40', '26#135', '25#25', '70#8', '2#193', '9#29', '19#38', '2#132', '7#14', '29#65', '8#39', '64#9', '30#14', '45#27', '10#105', '110#2', '78#17', '79#35', '83#6', '77#38', '50#34', '38#79', '15#36', '37#21', '44#40', '52#9', '56#50', '90#14', '40#32', '9#36', '80#34', '26#88', '56#86', '50#20']
تنها تو را مى پرستيم و تنها از تو يارى مى‌جوييم.

print(doc[1])
print(doc[1].head)
print(doc[1].dep_)
print(doc[1]._.dep_arc)
print(doc[1]._.root)
print(doc[1].lemma_)
print(doc[1].pos_)

نَعْبُدُ
وَ
معطوف بر محل
LTR
عبد

VERB

To jsonify the results you can use the following:

dictionary = language.to_json(pips, doc)
print(dictionary)

[{'id': 1, 'text': الْ, 'root': None, 'lemma': '', 'pos': 'INTJ', 'rel': 'تعریف', 'arc': 'RTL', 'head': حَمْدُ}, {'id': 2, 'text': حَمْدُ, 'root': 'حمد', 'lemma': '', 'pos': 'NOUN', 'rel': 'خبر', 'arc': 'LTR', 'head': *}, {'id': 3, 'text': لِ, 'root': None, 'lemma': '', 'pos': 'INTJ', 'rel': 'متعلق', 'arc': 'LTR', 'head': *}, {'id': 4, 'text': لَّهِ, 'root': 'أله', 'lemma': '', 'pos': 'NOUN', 'rel': 'نعت', 'arc': 'LTR', 'head': رَبِّ}, {'id': 5, 'text': رَبِّ, 'root': 'ربب', 'lemma': '', 'pos': 'NOUN', 'rel': 'مضاف الیه ', 'arc': 'LTR', 'head': عَالَمِینَ}, {'id': 6, 'text': الْ, 'root': None, 'lemma': '', 'pos': 'INTJ', 'rel': 'تعریف', 'arc': 'RTL', 'head': عَالَمِینَ}, {'id': 7, 'text': عَالَمِینَ, 'root': 'علم', 'lemma': '', 'pos': 'NOUN', 'rel': '', 'arc': None, 'head': عَالَمِینَ}, {'id': 8, 'text': *, 'root': None, 'lemma': '', 'pos': '', 'rel': '', 'arc': None, 'head': *}]

To jsonify the results you can use the following:

from spacy import displacy
from quranic_nlp import language
from quranic_nlp import utils

translation_translator = 'fa#1'
pips = 'dep,pos,root,lemma'

nlp = language.Pipeline(pips, translation_translator)
doc = nlp('1#4')
displacy.serve(doc, style="dep")
options = {"compact": True, "bg": "#09a3d5",
           "color": "white", "font": "xb-niloofar"}
displacy.serve(doc, style="dep", options=options)