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Python And Entity Name Recognition

Figure 1 – Python And Entity Name Recognition

In this article we will go over how to use Python and identify within it entity names. This process allows you to perfom textual analysis in your text and further enhance your workflows.

We are going to cover some libraries that do that using natural-language-processing a machine learning methodology which has been introduced the last few years to bring entity identification to a new level.

Natural language processing is essentially a form of machine learning but it specializes to the part where you perform these operations in big data. More particularly the big data is simple text patterns or simple text such as documents, web articles or even work operation manuals.

How Is Natural Language Processing Python Created

One of the main differences to it is that it relies entirely on text and has nothing to do with binary, this detail shifts the focus from a general training model to something very specific. For example you don’t need to account for random bytes in an image or a protocol that has a strange pattern that your training models need to understand and decipher.

Since the structure of it is known now you can specify a typical machine learning fail function and start training your models. One of the ways you can train your model data which we are going to see below is using the web. The web is a great resource for text and has a lot data so writing a simple scraping tool to extract this data and analyze it, makes a great feed into our natural language processing models.

Why Is Natural Language Processing With Python Used

Now that we covered how natural language processing in Python gets created using training models we will go over how that is being used today with Python and their libraries.

Below I will try to break down a list of reasons of why you may want to use Python with NLP.

  • Python is an easy scripting language to learn and pick up so using it with NLP makes your life easier overall
  • Python is supported natively in a lot of cloud applications such as:
    • AWS
    • Azure
    • Google cloud

You can find a great resource with step by step instructions and coding examples that shows you how to scale Python in the AWS cloud:

AWS Lambda With Python Complete Guide

This allows basically Python to work very well and scale when you are performing NLP operations. As they are resource and cpu intensive it is important to have some way of automatically scalability to meet your needs and these cloud services offer this.

  • Python already has some other libraries that synergize very well on performing textual analysis so they can be used as post-processing to facilitate better parsing the results of an NLP framework. Some examples of those are:
    • Opening and closing files
    • Identifying the text language. This is particularly important for NLP since it can support more languages so knowing if your text that you are analyzing is lets say Spanish rather than English helps because it allows you to load the correct language training model for your NLP framework. If you recall earlier we said that the NLP framework uses trained models, those trained models are specifically tied to the language you are using. So being able to detect the language using another Python library is very useful here.
    • Parsing specific documents. Python offers a great feature set of libraries that let you analyze and extract text from documents which we are going to list below. The above is very useful if you are writing a tool that tries to do data extraction because you don’t have to worry on parsing the documents.
      • Word documents
      • Excel
      • Powerpoint
      • HTML
      • PDF

There’s a great example of this that has step by step instructions with coding examples which you can find below:

Extract Human Names From Text With Python

What Python Libraries Exist For Natural Language Processing

Lets begin by saying that while Python has some generalized libraries to perform these operations such as Tensorflow and Pytorch it has some more specialized ones such as spaCy and NLTK.

Both frameworks can work together but it’s important to understand that each has their strengths and weaknesses. The most important strengths of spaCy is that it supports out of the box pre-trained models that support languages other than English. 

This difference is crucial if you are trying to perfom natural language processing in documents or web traffic that is not from an English speaking country as you may just be able to use a pre-trained model and save a lot of time. I have personally used it successfully with Spanish and I can attest the model works pretty good.

NLTK on the other hand has a big strength of doing very good grammar and word classification. So if you are trying lets say to detect verbs and people such as names in your text that means you will be able to make more deterministic decisions. A good example of NLP use for this is understanding the sentiment of some persona. Sentiment analysis for example has been used across the US elections recently and people were successfully able to tell the outcome from the reactions of the politicians on their tweets.

The possibilities here are endless and you can apply this same technology on anything you want. The idea of exit polls, monitoring what people are discussing on forums on social media is just one of the few examples that are good here. Since NLTK covers all your needs it’s a great pivot and starting point for this kind of code.


We were able to go over what frameworks Python supports for Natural Language Processing and explain some of their basic uses. Furthermore we outlined many reasons why Python and NLP is synergy very powerful that can solve even the most complex textual analysis problems. 

The field is constantly expanding and in the near future I just see this as being extended in many more scenarios. 

We will see more trained models and more advanced processing where using textual analysis basically initiates from speech recognition. Once you digitize your speech then you are basically entering the vessel of using NLP. This opens up the gates for robotics and other applications that essentially understand what a human can say or do automatically.