Glossary

spaCy

What is spaCy?

spaCy is an open-source Python library for natural language processing, developed by the Berlin-based company Explosion. It splits texts into words and sentences, determines parts of speech and base forms, analyses sentence structure and recognises named entities such as people, companies or places. Pre-trained models are available for many languages, including German. spaCy is designed for speed and for use in real applications, for example to pre-process large volumes of documents.

Topics

NLP
Python
open source

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Related terms

NLTK

NLTK (Natural Language Toolkit) is an open-source Python library for natural language processing, created at the University of Pennsylvania in the early 2000s. It provides tools for tokenisation, stemming, part-of-speech tagging and simple classification, as well as access to numerous text corpora. NLTK is used mainly in teaching and research; production applications often rely on libraries such as spaCy or on transformer models.

Python
language processing
+1

Named entity recognition (NER)

Named entity recognition (NER) is a natural language processing technique that finds names of people, companies and places, as well as dates or amounts, in text and assigns each to a category. It turns free text into structured data. Example: the sender’s company, contact person, delivery date and invoice amount are extracted automatically from an email and transferred to a CRM.

NLP
Information extraction
+1

Template

Dependency parsing

Dependency parsing is a natural language processing technique that represents the grammatical structure of a sentence as dependencies between words. Each word is linked to a governing word, for example subject and object to the verb: in 'The customer orders two lamps', 'customer' and 'lamps' depend on 'orders'. This helps with information extraction, for instance to identify who did what.

NLP
syntax
+1

Part-of-Speech Tagging (POS)

Part-of-speech tagging automatically assigns each word in a sentence its grammatical category, such as noun, verb, adjective or preposition. Because many words are ambiguous (“book” as a noun or a verb, “light” as an adjective or a noun), the methods take the sentence context into account. POS tagging is a classic pre-processing step for parsing, information extraction or spell checking and is offered by libraries such as spaCy and NLTK.

NLP
Grammar
+1