NLP or Natural Language Processing is one of the most focused upon learning models in modern times. This is especially due to how popular chatbots, sentiment analytics, virtual assistants, and translation tools have become. NLP empowers machines with the ability to process, understand and get meaning out of textual data, speech, or human language in general.
NLP allows other applications or programs to use human language. For example, the NLP model that powers Google understands what the user is searching for and fetches the results accordingly. Python online training can definitely help when one wishes to delve into NLP.
NLP models go much further than just finding the exact type of information and can also understand the context of the search or the reason and fetch similar or related results as well. NLP-powered machines can now identify the intent and sentiment behind the human language.
Developing Learning Models in Python
Python is a great language to use for NLP models as one can take the help of the NLTK package. The Natural Language Toolkit is an NLP package for Python. Additionally, you can also install the Matplotlib and NumPy libraries in order to create visualizations.
First, you need to have Python 3.5 or any of the later versions installed. After this, you must use pip install for installing packages such as NLTK, LXML, sklearn. If you decide to work with random data, you must first preprocess the data. You can use the NLTK library for text preprocessing and then carry on with analyzing the data.
Here are the 4 steps involved in developing a learning model using Python:
- Loading and data preprocessing
- Model definition
- Model training
- Model evaluation
How to Develop an NLP Model using Python
Let us learn how to develop an NLP Model in Python by creating a model that understands the context of a web page. Once you have installed the NLTK library, you should run this code to install the NLTK packages:
import nltk
nltk.download()
After this, you will be asked to choose the packages you wish to install, since all of them are of very small size, you can install all of them.
Then, you must find a web page that you want to process. Let us take the example of this page on computers. Now, you must use the urllib module for requesting websites:
import urllib.request
response = urllib.request.urlopen('https://computer.fandom.com/wiki/Main_Page)
html = response.read()
print(html)
Now, we can use the Beautiful Soup library for pulling the data out of the XML and HTML files. Also, this will help us clean the text of HTML tags.
Once, this is done, we can go ahead with converting the text into tokens using this:
tokens = [t for t in text.split()]
print(tokens)
Once the output is returned as tokens, we can use the FreqDist() function in the NLTK library for removing unnecessary words such as (for, the, at, a, and etc.) from our text and then plot a graph for the words that occur the most number of times. After this, the model identifies the most relevant words and then the context of the web page.
Conclusion
The auto-completion suggestions that we are given, the voice searches that our devices carry out for us are all possible with the advancements we have made in NLP. The PG in Data Analytics and Machine Learning offered by Imarticus is a great Data Analytics course with placement and can definitely help you delve deeper into concepts such as Deep Learning and ANN (Artificial Neural Network).
