Human communication contains an enormous amount of information, often nuanced with tone and emotion. Through the use of vocabulary, tone of voice, and subjects chosen, humans are experts at synthesizing data combinations to interpret, extract value and predict behaviors. As we all know, breakdowns in communication and misunderstandings do arise and sometimes we find it hard to make sense of people. Consider computers taking on these tasks of perception. Only around 20% of information is structured.
This is exactly where natural language processing algorithms come into play. Natural Language Processing or NLP describes the domain of artificial intelligence which provides machines with the ability to read, understand and extract meaning from human language. NLP helps computers figure out what we want from them.
The global NLP market shows steady growth and is expected to reach 43 billion by 2025. NLP algorithms have widespread application and are important tools for web developers r creating prototypes and advanced apps. NLP has the immense potential to unlock efficiencies in communications and is widely used by marketers in various applications, including helping people with disabilities.
Apple’s native NLP API NSLinguisticTagger presents a Foundation framework class that provides an interface for language processing. This framework has been introduced since iOS 5 and enables a number of benefits such as enhanced accuracy (around 90%), support of 52 languages, higher speed (up to 65,000 tokens/sec) and multi-thread, and high on-device optimization across different platforms. Since Apple prioritizes securing user data privacy the application of NSLinguisticTagger provides for data processing on the device only.
Python natural language processing library
You’ve probably heard about text processing in Python before. Python combines simplicity and power, which explains its popularity. This programming language has robust functionality for natural language data processing. People choose Python for several reasons:
- it’s object-oriented
- it’s dynamic
- semantics and syntax are transparent
- it comes with a huge standard library.
Python text manipulation begins as an interactive interpreter waiting for your input. Acquiring skills in Python is now as easy as going online and signing up for a class. Python course
NLP with Python is praised across the industry and is heavily utilized in scientific research. It is a magic wand which enhances quality, simplifies productivity, and enables the sustainability of software. Businesses find NLP with Python appealing because of the value-add of gaining insights into language-related problems your consumers might have. This allows companies to problem solve and find solutions. Google, Amazon and Facebook allocate millions of R&D dollar spend on developing the best NLP algorithms to refine their services
“Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another's code; too little and expressiveness is endangered”
— Guido van Rossum, Python programming language author.
Top NLP libraries
In the past, NLP data science was primarily developed by top-notch experts who mastered mathematics, linguistics, and specifically natural language processing machine learning. With the advent of libraries, access became more widespread. Now developers can use ready-made NLP toolkits for each specific task. A Python natural language processing library can be selected for a particular case. Here’s a list of the best ones:
- NLTK — a major tool for NLP and machine learning. Supports tokenization, classification, semantic reasoning, tagging and stemming.
- spaCy —one of the newer libraries. More accessible, provides the quickest syntactic parser, however, it supports lots of languages.
- TextBlob — good for newbies thanks to plain interface. Used in prototype design.
- polyglot — not a star, however, it covers a wide range of languages and provides extensive analysis.
- CoreNLP — a brainchild of Stanford University, it works well in product development.
NLTK vs. spaCy: who wins the battle?
What’s the best approach in choosing between NLTK or spaCy? n theory, both deal successfully with a wide range of NLP tasks. But in practice, different scenarios play out and require different approaches. NLTK has enjoyed dominance being an NLP standard in Python. Then spaCy disrupted this and found its own space. Here is a helpful comparison:
- Focus. NLTK sees things holistically, while spaCy is known for its granular approach. NLTK is used to develop complex NLP functions via different stemming libraries. In a nutshell, it’s a toolkit full of natural language processing algorithms. In contrast, spaCy uses a single stemmer and is more of a service used to complete concrete tasks. Given these philosophical differences, NLTK and spaCy are intended for different types of developers. The former is generally utilized by researchers to build something from scratch, while the latter is a good fit for app builders.
- Processing.This is a strings versus objects narrative. NLTK takes strings as input and returns lists of them as output. spaCy is object-oriented: every function returns objects as output. With NLTK, developers have to check out the documentation on a regular basis, while spaCy allows for easy exploration.
- Performance. In terms of speed, NLTK returns results considerably slower than spaCy: the latter was written in Cython from scratch. Also, spaCy exceeds NLTK with regard to part-of-speech tagging and word tokenization. The point is, NLTK approaches the text by splitting it into sentences. spaCy builds syntactic trees for each sentence and extracts more information.
While this provides a high level overview, there are other factors worth taking into account when deciding between the two. Social distancing and quarantine are the perfect time for self-education. Here’s a detailed spaCy tutorial for you and this NLTK tutorial is plain, well-explained and fun. Enjoy!
Ultimately, NLTK and spaCy offer a great variety of options. We don’t subscribe to a philosophy of picking one over the other because it depends on the circumstances. We also think they can be used together and combined in ways to maximize on the benefits of each. Feel free to combine and experiment. Ultimately, you’ll find what’s best for you.
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