Python Tutorial: Introduction to spaCy
Key Takeaways
Introduction to spaCy, a popular library for advanced natural language processing in Python, covering key concepts and getting started with the library
Full Transcript
hi I'm Edith I'm one of the core developers of Spacey a popular library for advanced natural language processing in Python in this video we'll take a look at the most important concepts of Spacey and how to get started at the center of Spacey is the object containing the processing pipeline we usually call this variable NOP for example to create an English NOP object you can import the English language class from spacy lang en and instantiate it you can use the NOP object like a function to analyze text it contains all the different components in the pipeline it also includes language specific rules used for tokenizing the text into words and punctuation Spacey supports a variety of languages that are available in space each Lang when you process a text with the NOP object Spacey creates a doc object short for document the doc lets you access information about the text in a structured way and no information is lost the dog behaves like a normal Python sequence by the way and lets you iterate over its tokens or get a token by its index but more on that later token objects represent the tokens in a document for example a word or a punctuation character to get a token at a specific position you can index into the doc token objects also provide various attributes that let you access more information about the tokens for example the dot text attribute returns the verbatim token text a span object is a slice of the document consisting of one or more tokens it's only a view of the doc and doesn't contain any data itself to create a span you can use Python slice notation for example 1 colon 3 will create a slice starting from the token at position 1 up to but not including the token at position 3 here you can see some of the available token attributes is the index of the token within the parent document text returns the token text is alpha is punk and like num return boolean values indicating whether the token consists of alphanumeric characters whether it's punctuation or whether it resembles a number for example the token 10 1 0 or the word 10 te n these attributes are also called lexical attributes they refer to the entry into vocabulary and don't depend on the tokens context let's see this in action and process your first tech
Original Description
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Hi, I'm Ines! I'm one of the core developers of spaCy, a popular library for advanced Natural Language Processing in Python.
In this video, we'll take a look at the most important concepts of spaCy and how to get started.
At the center of spaCy is the object containing the processing pipeline. We usually call this variable "nlp".
For example, to create an English nlp object, you can import the English language class from spacy dot lang dot en and instantiate it. You can use the nlp object like a function to analyze text.
It contains all the different components in the pipeline.
It also includes language-specific rules used for tokenizing the text into words and punctuation. spaCy supports a variety of languages that are available in spacy dot lang.
When you process a text with the nlp object, spaCy creates a Doc object – short for "document". The Doc lets you access information about the text in a structured way, and no information is lost.
The Doc behaves like a normal Python sequence by the way and lets you iterate over its tokens, or get a token by its index. But more on that later!
Token objects represent the tokens in a document – for example, a word or a punctuation character.
To get a token at a specific position, you can index into the Doc.
Token objects also provide various attributes that let you access more information about the tokens. For example, the dot text attribute returns the verbatim token text.
A Span object is a slice of the document consisting of one or more tokens. It's only a view of the Doc and doesn't contain any data itself.
To create a Span, you can use Python's slice notation. For example, 1 colon 3 will create a slice starting from the token at position 1, up to – but not including! – the token at position 3.
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