Natural Language Processing in Production: Converting PDF and Gutenberg

Estimates state that 70%–85% of the world’s data is text (unstructured data). Most of the English and EU business data formats as byte text, MS Word, or Adobe PDF. [1]

Organizations web displays of Adobe **Postscript Document Format **documents (PDF). [2]

In this blog, I detail the following :

Create a file path from the web file name and local file name;
Change byte encoded Gutenberg project file into a text corpus;
Change a PDF document into a text corpus;
Segment continuous text into a Corpus of word text.

Converting Popular Document Formats into Text

1. Create local filepath from the web filename or local filename

The following function will take either a local file name or a remote file URL and return a filepath object.

#in file_to_text.py
--------------------------------------------
from io import StringIO, BytesIO
import urllib

def file_or_url(pathfilename:str) -> Any:
    """
    Reurn filepath given local file or URL.
    Args:
        pathfilename:

    Returns:
        filepath odject istance

    """
    try:
        fp = open(pathfilename, mode="rb")  ## file(path, 'rb')
    except:
        pass
    else:
        url_text = urllib.request.urlopen(pathfilename).read()
        fp = BytesIO(url_text)
    return fp

2. Change Unicode Byte encoded file into a o Python Unicode String

You will often encounter text blob downloads in the size 8-bit Unicode format (in the romantic languages). You need to convert 8-bit Unicode into Python Unicode strings.

#in file_to_text.py
--------------------------------------------
def unicode_8_to_text(text: str) -> str:
    return text.decode("utf-8", "replace")
import urllib
from file_to_text import unicode_8_to_text
text_l = 250
text_url = r'http://www.gutenberg.org/files/74/74-0.txt' 
gutenberg_text =  urllib.request.urlopen(text_url).read()
%time gutenberg_text = unicode_8_to_text(gutenberg_text)
print('{}: size: {:g} \n {} \n'.format(0, len(gutenberg_text) ,gutenberg_text[:text_l]))
output =>

#nlp #python #machine-learning #programming #pdf

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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Ray Patel

1623250620

Introduction to Natural Language Processing

We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces an incomprehensible amount of data, a majority of which is unstructured. And ever since Big Data and Data Science have started gaining traction both in the IT and business domains, it has become crucial to making sense of this vast trove of raw, unstructured data to foster data-driven decisions and innovations. But how exactly are we able to give coherence to the unstructured data?

The answer is simple – through Natural Language Processing (NLP).

Natural Language Processing (NLP)

In simple terms, NLP refers to the ability of computers to understand human speech or text as it is spoken or written. In a more comprehensive way, natural language processing can be defined as a branch of Artificial Intelligence that enables computers to grasp, understand, interpret, and also manipulate the ways in which computers interact with humans and human languages. It draws inspiration both from computational linguistics and computer science to bridge the gap that exists between human language and a computer’s understanding.

Deep Learning: Dive into the World of Machine Learning!

The concept of natural language processing isn’t new – nearly seventy years ago, computer programmers made use of ‘punch cards’ to communicate with the computers. Now, however, we have smart personal assistants like Siri and Alexa with whom we can easily communicate in human terms. For instance, if you ask Siri, “Hey, Siri, play me the song Careless Whisper”, Siri will be quick to respond to you with an “Okay” or “Sure” and play the song for you! How cool is that?

Nope, it is not magic! It is solely possible because of NLP powered by AI, ML, and Deep Learning technologies. Let’s break it down for you – as you speak into your device, it becomes activated. Once activated, it executes a specific action to process your speech and understand it. Then, very cleverly, it responds to you with a well-articulated reply in a human-like voice. And the most impressive thing is that all of this is done in less than five seconds!

#artificial intelligence #big data #data sciences #machine learning #natural language processing #introduction to natural language processing

Paula Hall

1623392820

Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.

Introduction to Spacy

spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.

After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.

import spacy
nlp = spacy.load("en_core_web_sm")

We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.

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Sival Alethea

1624381200

Natural Language Processing (NLP) Tutorial with Python & NLTK

This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=X2vAabgKiuM&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=16
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Chet Lubowitz

1598400240