spaCy vs NLTK - Which is the better Choice for NLP?

Jan Schäfer

To use natural language processing (NLP), companies need the right tool. In addition to Gensim, Ernie (Baidu) and Bert (Google), the Python libraries spaCy and NLTK have established themselves for this purpose. 

In our comparison of spaCy vs NLTK, we explain in a practical way when which library is the right choice for efficiently understanding and processing human language data. We also show you code examples of how you can carry out tokenization, parts of speech tagging and entity detection with spaCy and NLTK - and thus decide on the right NLP tool.

The most Important in a Nutshell

  • spaCy is like a service that developers use to solve specific problems. The library is therefore particularly suitable for production environments.
  • NLTK is like a large toolbox with which developers can choose from many different solutions for a problem. The library is therefore aimed particularly at scientists.
  • Konfuzio is your powerful partner for using spaCy and NLTK to efficiently analyze language in documents. Try Konfuzio now for free! 

What is spaCy?

spaCy is an open source library for the Python programming language. It was developed by Matthew Honnibal and Ines Montani, the founders of the software company Explosion, for natural language processing (NLP). spaCy uses techniques such as tokenization, part-of-speech (POS) tagging and lemmatization to analyse texts. 

What is NLTK?

The Natural Language Toolkit (NLTK) is a collection of libraries and programs for the Python programming language. It was originally developed by Steven Bird, Ewan Klein and Edward Loper for applications in computational linguistics. Like spaCy, it provides the basic functions for NLP. NLTK is open source and is distributed under the Apache license. 

spacy vs nltk comparison

spaCy vs NLTK - Comparison of relevant application aspects

To decide when spaCy and when NLTK is the better choice for NLP, let's take a look at 5 important aspects of libraries:

Functionality and features

spaCy: spaCy is structured like a service. This means that it provides a precise solution for every problem. In practice, this means that developers can complete specific tasks quickly and easily with spaCy. In addition to the basic NLP functions, the library has various extensions and visualization tools such as displaCy or displaCyENT. It also contains pre-trained models for various languages. In total, spaCy supports more than 60 languages, including German, English, Spanish, Portuguese, Italian, French, Dutch and Greek. 

NLTK: NLTK is a large toolbox of NLP algorithms. In practice, this means that developers can choose from a variety of solutions to a problem and test them out. In addition to the classic NLP functions, the library offers access to a large number of corpora and resources for NLP research. In total, NLTK supports over 20 languages, including German, English, French, Spanish, Portuguese, Italian, Greek and Dutch.

Performance and speed

spaCy: SpaCy is known for its high speed and efficiency. The developers Honnibal and Montani have optimized the library to quickly process large amounts of text data. 

NLTK: NLTK offers a solid performance, but tends to be slower than spaCy, especially when processing large amounts of text.

Ease of use

spaCy: Developers praise SpaCy for its user-friendliness. It offers an intuitive API and well-documented functions that make it easy even for beginners to quickly work productively with the library.

NLTK: NLTK is significantly more comprehensive than spaCy. The variety of functions available can therefore be overwhelming for beginners. In addition, the library often requires more code to perform certain NLP tasks, which makes it more challenging for beginners.

Community support

spaCy: SpaCy has a constantly growing and committed community of developers and researchers. There is an active mailing list, online forums and social media where users can ask questions. The community also develops and shares external extensions and plugins. Particularly popular points of contact for developers include the GitHub Forum, Stack Overflow for spacY and the spaCy Github Repository.

NLTK: NLTK has been an established library for a long time and therefore also has a large and diverse community. There are numerous resources such as tutorials, books and online discussion forums created by experienced members of the community. Popular places to go, for example, are the NLTK Google Group and the NLTK GitHub Repository.

Customization options

spaCy: SpaCy allows developers to train custom models for NLP tasks such as Named Entity Recognition (NER) and provides tools for fine-tuning existing models. This flexibility makes spaCy particularly suitable for projects that need to recognize specific entities or terminology.

NLTK: NLTK offers a wide range of algorithms and tools that allow developers to create customized NLP applications. It enables the training of models for various tasks such as classification and sentiment analysis. With its modular structure, NLTK allows in-depth customization and implementation of specific algorithms for advanced research projects.

spaCy vs NLTK - Result

Our spaCy NLTK comparison shows: Developers use spaCy to implement functions efficiently. The library is therefore less of a tool and more of a service. It is particularly suitable for production environments such as app development. NLTK, on the other hand, allows developers to choose from a wide range of algorithms for a problem and easily extend the library modules. NLTK thus enables developers to work as flexibly as possible. The library is therefore primarily aimed at scientists and researchers who want to develop models from scratch.

spacy vs nltk code examples

spaCy vs NLTK - Tokenization, POS Tagging and Entity Detection

How the two Python libraries work and the advantages this generates for your company are therefore obvious. What does the application look like in practice for developers? Let's take a look at tokenization, parts of speech tagging and entity detection - 3 essential NLP techniques that are used in various phases of language processing:

Tokenization

Tokenization is the first step in NLP processing. Developers use it to break down a text into smaller units, so-called tokens. These tokens can be words, punctuation marks or other linguistic units. This makes it easier to handle texts. Only after tokenization are developers able to subject the text to further processing steps.

Tokenization with spaCy

The following example shows how to carry out tokenization with spaCy:

import spacy
# Load the spaCy model for the English language
nlp = spacy.load("en_core_web_sm")
# Sample text to be tokenized
text = "SpaCy is a powerful Python library for natural language processing."
# Process the text using spaCy
doc = nlp(text)
# Tokenize the text and print each token
for token in doc:
    print(token.text)

In this example, we use the en_core_web_sm model to tokenize the sample text. The NLP object processes the text and then each token in the processed document is ejected in a loop. You can replace the variable "text" with any text that you want to tokenize with spaCy.

Tokenization with NLTK

IN NLTK, an example of tokenization looks like this:

import nltk
# Sample text for tokenization
text = "NLTK is a leading platform for building Python programs to work with human language data."
# Tokenize the text into words
tokens = nltk.word_tokenize(text)
# Print the tokens
print(tokens)

In this code, we first import the nltk library and then define a sample text string that we want to tokenize: "NLTK is a leading platform for creating Python programs to process human language data."

The nltk.word_tokenize() function is used to tokenize the input text into individual words. When you run the code, the variable tokens contains a list of tokens that represent each word in the input text. Here is the output you would get if you display the token list:

['NLTK', 'is', 'a', 'leading', 'platform', 'for', 'building', 'Python', 'programs', 'to', 'work', 'with', 'human', 'language', 'data', '.']

In this output, NLTK has tokenized the input text into individual words. Each word in this list is an element. Punctuation marks such as dots are also considered as separate tokens in this process.

Parts of Speech (POS) Tagging

Tokenization is usually followed by parts of speech tagging. This assigns grammatical parts of speech to the tokens, such as nouns, verbs and adjectives. This information is important in order to understand the syntactic structure of a sentence. Parts of speech tagging is particularly useful for tasks such as text analysis, text translation and language generation, as it helps to understand the relationship between the words in the sentence.

Parts of Speech (POS) tagging with spaCy

A code example for carrying out POS tagging with spaCy looks like this:

import spacy
# Load the spaCy model (English)
nlp = spacy.load("en_core_web_sm")
# Sample text for POS tagging
text = "SpaCy is a popular Python library for natural language processing."
# Process the text using spaCy
doc = nlp(text)
# Print the token and its POS tag for each word in the text
for token in doc:
    print(token.text, token.pos_)

In this example, we use the en_core_web_sm model for English language processing. The NLP object processes the input text and then outputs the POS tags for each token in the text with token.text and token.pos_. 

Parts of Speech (POS) tagging with NLTK

With NLTK, POS tagging looks like this, for example:

import nltk
from nltk import word_tokenize, pos_tag
# Download NLTK data (if not already downloaded)
nltk.download('dot')
nltk.download('averaged_perceptron_tagger')
# Sample text
text = "NLTK is a powerful library for natural language processing."
# Perform POS tagging
pos_tags = pos_tag(word_tokenize(text))
# Display the POS tags
print(pos_tags)

In this example, we use the pos_tag function to assign POS tags to the tokens. The POS tags consist of tuples, where each tuple contains a word and the corresponding POS tag.

Entity Detection

Entity detection is another step in NLP processing that aims to recognize and classify named entities such as people, places, organizations and other specific information in text. Entity detection makes it possible to extract important information from the text and is particularly useful for applications such as automatic indexing of documents and question-answering systems.

Entity detection with spaCy

This is how you perform entity detection with spaCy, for example:

import spacy
# Load the spaCy model for English
nlp = spacy.load("en_core_web_sm")
# Sample text for entity detection
text = "Apple Inc. was founded by Steve Jobs in Cupertino. The iPhone was released in 2007."
# Process the text with spaCy
doc = nlp(text)
# Iterate through the entities and print them
for ent in doc.ents:
    print(f "Entity: {ent.text}, Type: {ent.label_}")

In this example, we load the English language SpaCy model (en_core_web_sm), process the sample text and then run through the recognized entities, printing both the entity text and the corresponding entity type. The entity types can include categories such as personal names, organizations and locations.

Entity detection with NLTK

With NLTK, entity detection for the same example looks like this:

import nltk
from nltk import pos_tag, ne_chunk
from nltk.tokenize import word_tokenize
# Sample text
text = "Barack Obama was born in Hawaii. He served as the 44th President of the United States."
# Perform part-of-speech tagging
pos_tags = pos_tag(word_tokenize(text))
# Perform named entity recognition
tree = ne_chunk(pos_tags)
# Display named entities
for subtree in tree:
    if isinstance(subtree, nltk.Tree):
        entity = " ".join([word for word, tag in subtree.leaves()])
        label = subtree.label()
        print(f "Entity: {entity}, Label: {label}")

In this example, we use the ne_chunk function to identify named entities in the text. The result is a tree structure. We iterate through the tree to extract and eject the named entities along with their labels.

spaCy and NLTK - Efficient Use with Konfuzio

Konfuzio has a Python SDK that allows developers to program their own document workflows. In this way, you are able to apply the functionality of spaCy and NLTK to documents - exactly as you need it for your individual use case. 

Konfuzio not only makes it possible to analyze large volumes of text in documents, but also to recognize and process layout elements. To do this, the German provider relies on pioneering technologies such as NLP, OCR, Machine Learning and Computer Vision.

In practice, Konfuzio is therefore suitable for all industries in which companies need to process large volumes of data from documents. 

A classic example is its use in the legal sector. Law firms use individualized document workflows to analyze, structure and classify legal documents. In this way, lawyers are able to understand legal texts efficiently, extract key terms and identify relevant information. This reduces the processing time per case and therefore saves costs.

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