spaCy is one of the leading Python libraries for Natural Language Processing (NLP). Find out all you need to know: presentation, features, benefits, training...
Natural language processing, or NLP, is a branch of artificial intelligence that is becoming increasingly popular. Generally speaking, it concerns all forms of interaction between computers and human language. It encompasses the analysis, understanding and meaning extraction of human language for computers.
In particular, this technology makes it possible to automatically analyze texts in everyday language, to understand their meaning, to quickly identify key information, or to find similarities between several texts.
In the age of Big Data, companies are faced with huge volumes of unstructured text data. These can come, for example, from social networks and reviews left on the web.
NLP enables this unstructured data to be represented in a form that can be understood by computers, and is therefore suitable for analysis. It enables the automatic extraction of information from documents. Use cases include automatic summarization, named entity recognition, question answering systems and sentiment analysis.
This technology is at the heart of many artificial intelligence applications. Put simply, it enables computers to understand, process and produce language in the same way as a human.
The fundamental tasks of NLP include tokenization, lemmatization, segmentation and POS tagging. In the past, to perform these tasks, developers and researchers had to develop their own programs. Today, there are many libraries available to simplify Natural Language Processing tasks. One of the most popular is spaCy.
What is spaCy ?
spaCy is a free, open-source Python library published under the MIT license for Natural Language Processing (NLP). It is written in Python, and designed for production use thanks to a concise, easy-to-use API.
The library was originally developed by Matt Honnibal of Explosion AI. For connoisseurs of the Python language, spaCy can be seen as the NLP equivalent of numPy: a low-level, yet intuitive and powerful library.
This tool makes it possible to create applications for processing and understanding large volumes of text. In particular, it can be used to develop systems for information extraction and natural language understanding, or to pre-process texts for Deep Learning.
spaCy tools and features
spaCy can be used for a wide variety of NLP-related tasks. These include tokenization, lemmatization, POS tagging, sentence or entity recognition, dependency analysis, word/vector transformation and other normalization and cleaning techniques.
If these terms seem abstruse to you, don’t worry. That’s perfectly normal if you’re new to Natural Language Processing. Here’s a more detailed overview of the various spaCy features.
Tokenization consists in breaking down a portion of text into words, spaces, symbols, punctuation and other elements to create “tokens”. This is a fundamental step in most NLP tasks.
Lemmatization is directly linked to tokenization, and enables a word to be reduced to its basic form. Suffixes, prefixes and past participles can be removed to find the root of the term. This process is particularly useful for Machine Learning, and especially for text classification.
Tagging part-of-speech (POS) is a process for assigning grammatical properties such as nouns, verbs, adverbs or adjectives to words. Words sharing the same POS tags generally follow the same syntactic structure and are useful for rule-based processes;
Entity recognition is the process of classifying named entities in a text into different predefined categories. These may be people, places or dates, for example. spaCy’s statistical model can be used to classify a wide variety of entities, including people, entities, works of art and nationalities.
Dependency parsing is a method for driving the dependency parsing of a sentence. This reveals its grammatical format. This technique highlights the relationships between main words and their dependencies.
Finally, word-vector representation helps machines to understand and interpret the links between words in a human-like way. The numerical representation of a word highlights its relationships with other words.
spaCy vs NLTK
Besides spaCy, the other most popular Python library for NLP is NLTK (Natural Language Toolkit). There are, however, important differences between these two resources.
First of all, spaCy groups together various algorithms adapted to different problems in its toolbox. These algorithms are managed and renovated by the library.
NLTK, on the other hand, lets you choose from a large number of algorithms, depending on the problem to be solved.
Another major difference is that spaCy uses statistical models for seven languages: French, English, German, Spanish, Italian, Portuguese and Dutch. NLTK supports many different languages.
When analyzing text, such as sentiment analysis, spaCy deploys an object-oriented strategy. Words and phrases are treated as objects. In contrast, NLTK is a line-processing library. It receives inputs and returns outputs in the form of lines of code.
Finally, each of these two libraries has its own specialty. For tokenization and POS tagging, spaCy offers better results and features the latest, most powerful algorithms. NLTK, on the other hand, is superior for sentence tokenization.
spaCy's limits
spaCy offers many possibilities, but it’s important to understand its limitations. First of all, it’s not a platform or API. It is not offered as software or as an application, but as a library to simplify the development of NLP applications.
Nor is it an engine for creating chatbots or voice assistants. This library can be used to power NLP conversation applications, but only provides the underlying word-processing capabilities.
Nor is it designed for research or teaching, unlike NLTK or CoreNLP. This explains one of the main differences, namely that spaCy avoids asking the user to choose between multiple algorithms.
How do I learn to use spaCy? Training courses
Learning to master spaCy is very useful, if not indispensable, for working in the field of Artificial Intelligence and Natural Language Processing. It’s a skill that’s increasingly in demand.
To acquire it, you can turn to DataScientest training courses. Python programming and Machine Learning are at the heart of our Data Scientist, Data Analyst and Data Engineer courses. On these courses, you’ll learn how to use Python and its various libraries to develop AI models.
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