Rounak Jain Jun 10, 2020 No Comments
Chatbots are a common feature on any website these days. We visit a site, and a chatbot pops up encouraging us to interact. Many of us will find this intriguing. A Data Science enthusiast will probably go ahead and look for the underlying concept of Natural Language Processing (NLP). I have recently done a lot of work in this interesting field of NLP using Python. It has been an amazing experience so far and I am very excited to share these learnings with you all. In this article, we commence with the Introduction to Natural Language Processing using Python. Gradually, I will usher you deep into its various interesting concepts and related codes. So let us get started.
Natural Language Processing is a branch of Artificial Intelligence. To put it differently, Natural Language Processing enables a computer to process and understand the human language in a way we speak.
We see a lot of languages commonly spoken. For example, English, Mandarin, Spanish, Hindi, Arabic, Russian, and many more. All the languages comprise of words, phrases, clauses, grammar, semantics, syntaxes, parts of speech, etc. Ever since the 1950s, we are working on imparting computers with the ability to process the various human languages. We have come a long way today in the area of Text analytics, thanks to Statistical Modelling, Machine Learning, and Deep Learning. Let us go through the various interesting NLP techniques and operations that empowers us with various automation when it comes to processing a huge number of documents.
A few prominent and widely used techniques of Natural Language Processing are as follows.
One of the most challenging problems in the NLP domain is text or document classification. We try to categorize text documents into various predefined categories based on the inherent properties of each document. This technique finds applications in email spam identification and news categorization where we can group similar email or news documents into predecided categories or classes. We can also extend the technique to other content like music, video, images, etc.
Text clustering is different from the application of supervised machine learning for document classification. This technique follows unsupervised machine learning to cluster similar documents together as we do not have any predefined categories. Text clustering comes in handy when we have a large number of documents and want to create document clusters and gain quick insights.
The text summarization technique is in huge demand because of the availability of a huge amount of unstructured text documents. This gives a quick summary of large text documents and helps save a lot of time in reading and comprehension. Two main types of techniques for text summarization include extraction-based summarization and abstraction-based summarization.
This technique needs no introduction. Innumerable product-based companies, apps, online series, news organizations, Telecomm service providers, and much more continuously analyze comments and responses of their customers and viewers on social media platforms like Twitter, Facebook, etc. The intention is to identify customer sentiments related to their products and services. Consequently, they can work on problem areas and improve their services accordingly. Sentiment Analysis is highly in demand and sure a very interesting feature of NLP.
Documents often comprise of text which talks about names, places, dates, organizations, etc. The Named Entity Extraction and Recognition aim at helping us identify all of them in our text documents.
It is amazing to know how NLP helps us gather all important information and quick insights from our documents with a few lines of codes. Let us see how Python helps us do that.
If you look around carefully, you can easily notice various applications of NLP used in day-to-day life. Chatbots is but one I mentioned earlier. Other amazing NLP applications are as follows.
Speech Recognition – The widely known Cortana, Alexa, Siri virtual assistants uses the speech recognition application of NLP.
Machine Translation – Google Translate is one of the very reliable features from Google that applies Machine Translation
Question Answering – All the Virtual Assistants is useless without the Question Answering application
Spell Check – Grammarly extensively uses this Spell Check application
Text Summarization – Inshorts is a popular application that has implemented the Text Summarization technique
Another amazing natural language processing application is SignAll that converts sign language into text. This can help individuals who are deaf communicate with those who don’t know sign language. There are a lot more ways in which NLP is applied. Let us know your experience with NLP in the comments box.
Earlier, only experts were able to work on Natural Language Processing projects as it required sound knowledge of Mathematics, Machine Learning, and linguistics. Today we can apply NLP easily because of the availability of various tools that simplifies text processing massively. This introduction to Natural Language Processing using Python will be incomplete if we do not talk about the awesome libraries related to NLP Python provides. The top 5 libraries to discuss are:
NLTK is a very popular library Python programmers wield as an NLP beginner. This library provides a lot of operations like stemming, lemmatization, tokenization, Part of speech tagging, etc. To know more about NLTK, you can visit this site.
Another very useful library for text data analytics is Textblob. This library can perform Sentiment Analysis, Parts of speech tagging, n-grams, tokenization, parsing, classification, translation, and many more. In other words, it provides a simple API for diving into common natural language processing (NLP) tasks.
Stanford University has created the CoreNLP library using Java language to perform on several NLP features. Its goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. The package is highly flexible, easily extensible, and has got fast processing speed. It is interesting to note that CoreNLP even improvises the efficiency of NLTK massively when coupled together.
A robust Natural Language Processing library that can process the massive volumes of text is Gensim. It allows us with efficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning. Visit here to know more about this amazing processing speed and memory-efficient library.
SpaCy is a relatively new package written in Cython and is pretty fast and efficient. It supports 56+ languages, easily integrates with Deep Learning, provides pre-trained vectors, and many more features. SpaCy is fast gaining popularity as it allows us to implement advanced NLP easily.
This is just a small list of libraries available in Python. Additionally, libraries like Polyglot, Quepy, PyNLPl, Pattern, and Flair are also used. You might be wondering about the Natural Language Processing packages R has to offer. Interestingly, there are several amazing libraries like OpenNLP, RWeka, KoRpus, LanguageR, Maxent, RKEA, lSA, etc. with which one can work on NLP.
Interested in having a glance at the entire NLP landscape along with Jupyter Notebook Python codes? This Github link here has it all.
To simultaneously learn some interesting tricks in Python, you can visit our blog Top 15 interesting tricks every Python beginner must know.
I hope you liked the Introduction to Natural Langauge Processing using Python. In our next article, we will be working on some operations to be performed on strings. This will help us set rolling for further concepts of NLP using various libraries mentioned in our subsequent articles. Stay tuned!!!