NLP: Python Tools and Libraries

* Before reading through our piece on common Python tools and libraries, make sure to check out the previous four installments of this series: “Common NLP Techniques,” “What is Natural Language Processing,” “Natural Language Processing Applications, and “What are the Top NLP Language Models.”

What’s this about: Natural language processing (NLP) is focused on training data models with insights extracted from text. As covered in the previous installment of this series, NLP is used for applications like sentiment analysis, named entity recognition, and text summarization. With the wide range of NLP tools and libraries that are now available to developers, the range of NLP applications are also being expanded. These libraries are crucial for anyone looking to develop technologies like chatbots, speech recognition, and patient data processing. 

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What is an NLP Library? 

An NLP library has one fundamental goal: to simplify text processing. The best libraries can convert text into structured features, which can then be fed into machine learning (ML) and deep learning (DL) systems. 

NLP libraries changed the game for NLP. Previously, these types of projects required professionals with a deep level of expertise in the areas of mathematics, machine learning, and linguistics. With the development of ready-made tools, text preprocessing was dramatically simplified so that these same experts could turn their focus to building machine learning models. Standardized tools and algorithms provide immediate benefits like consistent results and multi language support, which is especially useful for beginners.

Python is the top programming language for NLP projects for many reasons:

  • simple syntax;

  • transparent semantics;

  • excellent support for integration;

  • versatility;

  • large number of open source libraries (Scikit-learn, Torch, FastAI, Theano, TensorFlow, etc.);

  • and useful tools for machine learning techniques.

The versatile Python language provides developers with a wide range of NLP tools and libraries that are crucial for NLP tasks like document classification, part-of-speech (POS) tagging, word vectors, sentiment analysis, topic modeling, and text summarization.

Here is a look at some of the top NLP libraries on the market:

Natural Language Toolkit

One of the leading platforms for building Python programs that analyze human language data, the Natural Language Toolkit (NLTK) supports a wide range of tasks like classification, tagging, stemming, parsing, semantic reasoning, and tokenization in Python. It is considered “the” main tool for NLP and machine learning, and it is the introduction for many developers looking to get into the field. 

NLTK offers simple interfaces to over 50 corpus and lexical resources, and the tool’s essential functionalities are useful for nearly all NLP tasks with Python. With that said, NLTK does take time to learn. But once you have a hold on the tool, it opens up many opportunities for NLP models. 

NLTK use core cases: 

  • Sentiment analysis

  • Developing chatbots

  • Removing stop words and persons names in a recommendation system

spaCy

An open-source NLP library in Python, spaCy is designed explicitly for production usage. It enables developers to create applications that can process and understand large volumes of text. 

spaCy is used to preprocess text for Deep Learning, and it can help build natural language understanding systems and information extraction systems. Loaded with pre-trained statistical models and word vectors, spaCy supports tokenization for over 49 languages.

Unless you grew up with today’s highly advanced tech, you likely remember the days when our smartphone autocorrect feature was still in its infancy. It was the source of countless embarrassing situations for many of us, and it made for some great online content. Tools like spaCy have helped NLP come a long way with autocorrect - saving us all!

spaCy core use cases: 

  • Search autocomplete and autocorrect

  • Automatic summarization of resumes

  • Analyzing online reviews and extracting key topics

Gensim

Designed specifically for topic modeling, document indexing, and similarity retrieval with large corpora, Gensim is another one of the top Python libraries for NLP. The algorithms in Gensim enable it to process input larger than RAM, and its intuitive interfaces help it achieve efficient multicore implementings of popular algorithms like Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). 

Gensim is often used for text summarization, so if you are one of us who likes to far exceed a given character count for an essay or article, Gensim could be the right choice!

Genesim core use cases: 

  • Text summarization

  • Finding text similarity

  • Converting words and documents to vectors

TextBlob

TextBlob is one of the best libraries for developers looking to start with NLP in Python, and it is a good prep for NLTK. The easy interface helps beginners learn basic NLP tasks like pos-tagging, sentiment analysis, and noun phrase extraction. TextBlob objects are able to be treated as Python strings that are trained in NLP. 

TextBlob is also useful for translations, and that is no easy task. Machine translation can go terribly wrong (just check out this Jimmy Fallon sketch), so it is crucial for developers to use the best tools to achieve the most accurate results. After all, we’re talking about over 7,000 human languages across the globe! 

The practical library is concentrated on day-to-day usage, and it is especially useful for initial prototyping in almost all NLP projects. However, TextBlob does have some pitfalls. It inherits low performance from NLTK, which means it’s not recommended for large scale production usage. 

TextBlob core use cases: 

  • Sentiment analysis

  • Translation and language detection

  • Spelling correction

Pattern

The Python tool Pattern is used for text processing, web mining, machine learning, NLP, and network analysis. While Pattern is an excellent choice for NLP, it’s important to note that it hasn’t been updated in three years. The full source code is available for developers to modify, update, and customize the library on their own. 

One of the most valuable aspects of Pattern is its collection of features that make it one of the most useful NLP libraries in Python. These include finding superlatives and comparatives, and fact and opinion detection. 

Pattern includes modules for data mining from social networks, search engines, and Wikipedia. It also has a straightforward syntax that makes it useful for both scientific and non-scientific audiences, and the function names and parameters make it so that the commands are self-explanatory. 

Pattern core use cases: 

  • Spelling corrections

  • Search engine results with APIs

  • Finding sentiments

  • HTML Data to Plain Text

Other Libraries

These are not the only available libraries; there are many more useful ones for developers to choose from. Two of these are Stanford's CoreNLP and Polyglot. 

Data analysis is easier and more efficient with CoreNLP. The tool only requires a few lines of code for the extraction of various text properties like named-entity recognition and part-of-speech tagging, and it supports a wide range of programming languages, such as Python. It can also be used for other natural languages besides English, as it supports Arabic, Chinese, German, French, and Spanish. 

The Python NLP package Polyglot supports multilingual applications while offering a wide range of analysis. Some of the most popular features include language detection, tokenization, named-entity recognition, part-of-speech tagging, and sentiment analysis. 

The Perfect Pairing

NLP has come a long way in a short amount of time, and a lot of that can be owed to the wide range of useful Python tools and libraries available. Python is a top technology that enables us to develop software capable of handling natural languages, which is extremely impressive given the highly complex nature of language. 

These different tools and libraries are revolutionizing the field as they enable developers of all skill levels to get involved. This will undoubtedly lead to even more exciting and accurate NLP technologies in the near future!

>>> Look out for the next installment of this NLP series in the coming weeks! I’ll be diving into natural language disparity within the field and why it’s crucial to go beyond English. 

>>> If you want to gain more insight into NLP and other artificial intelligence technologies, make sure to sign up for the Medium blog: https://gcmori.medium.com/membership

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Giancarlo Mori