Welcome to the Quranic Arabic Corpus, an annotated linguistic resource which shows the Arabic grammar, syntax and morphology for each word in the Holy Quran. The corpus provides three levels of analysis: morphological annotation, a syntactic treebank and a semantic ontology.
The Quran
A resource for anyone looking to understand the Sacred Text of Islam; the world's leading online source of Quran translation and commentary. Explore, read and search publications in many languages.
Neither Corpus Nor Edition: Building a Pipeline to Make Data Analysis Possible on Medieval Arabic Commentary Traditions
We have built a suite of tools in Python to proficiently analyze text reuse and intertextuality for a specific kind of set of medieval Arabic texts (commentaries) available in print. We take these printed editions, scan them, pre-process the images, give it to an OCR engine, clean the results, and store it in a data structure that mimics the explicit intertextual relation the texts have, and continue to perform data analysis on it. Digital approaches to medieval Arabic texts have either been at the micro-level in what has become known as a ‘digital edition’, i.e. the digital representation of one text, densely annotated, most commonly in TEI-XML, or it has been done at the macro-level in what is called a ‘digital corpus’, consisting of thousands of loosely encoded and sparsely annotated plain text files, accompanied by an entire infrastructure and high-performing software to perform broadly scoped queries. The micro-level generally is at the level of tens of thousands of words while the macro-level can be at the level of over a billion words. The micro-level is explicitly designed to be human readable first, while the macro-level is built to be machine readable first. At the micro-level, every little detail needs to be correct and in order, while at the macro-level a fairly large margin of error is still negligible as a mere rounding error. Amidst these levels we have been seeking a meso-level of digital analysis: neither edition nor corpus, but rather a group of texts at the level of hundreds of thousands to millions of words, with a small but perceptible margin of error, and a light but noticeable level of annotations, principally geared towards machine readability, but with ample opportunity for visual inspection and manual correction. In this paper we explain the rationale for our approach, the technical achievements it has led us to, and the results we so far obtained.
Approaching the Qur'an | Oneworld
Approaching the Qur’an presents brilliant translations of the short, hymnic chapters, or suras, associated with the first revelations to the Prophet Muhammad. These early suras contain some of the most powerful, prophetic and revelatory passages in religious history, offering the vision of a meaningful and just life that anchors the faith of one fifth of the world’s inhabitants. In addition to these translations, Michael Sells provides an introduction to the Qur’an, commentaries on the suras, a glossary of technical terms and discussion of the auditory nature and gender aspects of the Arabic text. An ideal resource for students and interested lay readers, the work is enriched by free audio downloads of Qur’anic reciters chanting several of the early suras, allowing readers to hear the Qur’an in its original form. This third edition also includes a new full sura and associated commentary, a new preface and a thoroughly updated bibliography.
Surah Ali 'Imran — Full Surah, Read, Listen and Study - Quran.com
Read and listen to Surah Ali 'Imran in full, chapter 3 of the Quran with 200 verses. Explore translation, tafsir and word-by-word meaning.
atproto made simple: publishing lexicons - underreacted
i find some atproto documentation extremely confusing. the section about publishing lexicons is one of those parts that makes me want to tear my hair out. it…
An interactive introduction to the terrific experience of rendering Arabic typography and its technical debt
This post was discussed in Lobsters and HackerNews
Natural Language Processing With Python's NLTK Package – Real Python
In this beginner-friendly tutorial, you'll take your first steps with Natural Language Processing (NLP) and Python's Natural Language Toolkit (NLTK). You'll learn how to process unstructured data in order to be able to analyze it and draw conclusions from it.
