Need advice about which tool to choose?Ask the StackShare community!

Amazon Comprehend

50
138
+ 1
0
SpaCy

217
291
+ 1
14
Add tool

Amazon Comprehend vs SpaCy: What are the differences?

Introduction

In this article, we will compare and provide key differences between Amazon Comprehend and SpaCy, two popular natural language processing (NLP) tools. By examining their features and capabilities, we can better understand their unique advantages and applications in various scenarios.

  1. Pre-trained Models: Amazon Comprehend comes with pre-trained models for various tasks such as sentiment analysis, entity recognition, keyphrase extraction, language detection, and topic modeling, enabling faster development and deployment. In contrast, SpaCy provides pre-trained models mainly for part-of-speech tagging, dependency parsing, and named entity recognition, requiring additional training or external models for other tasks.

  2. Customization: While both Amazon Comprehend and SpaCy allow customization to some extent, SpaCy provides more flexibility in training and fine-tuning models on specific domains and languages. It offers a trainable pipeline, allowing users to train models on their own data and thus adapt the NLP capabilities to their particular needs. On the other hand, Amazon Comprehend suits well for users who prefer a more out-of-the-box solution without extensive customization.

  3. API and Integration: Amazon Comprehend provides a robust API that allows seamless integration with other AWS services and platforms. It offers the capability to easily analyze large volumes of text data by utilizing cloud-based infrastructure. Meanwhile, SpaCy, being an open-source library, provides APIs that can be integrated into custom applications or workflows, providing more control and customization options for developers.

  4. Language Support: Amazon Comprehend supports a wide range of languages, including English, Spanish, French, German, Italian, Portuguese, and many more. It provides NLP capabilities for text analysis in several languages, empowering multilingual applications. In comparison, SpaCy supports a lesser number of languages, primarily focusing on English, German, French, Spanish, Portuguese, Italian, Dutch, and multi-language models.

  5. Domain-specific Features: Amazon Comprehend offers domain-specific features such as medical entity recognition, enabling the extraction of medical information from unstructured text. It also provides features for identifying Personally Identifiable Information (PII), enabling compliance with data privacy regulations. In contrast, SpaCy focuses more on generic NLP tasks and lacks domain-specific features out-of-the-box.

  6. Pricing Model: The pricing model for Amazon Comprehend is based on the number of units of text processed, including the total number of characters analyzed. On the other hand, SpaCy is an open-source library that can be used freely without any specific pricing. However, users need to be mindful of their computational resources and infrastructure costs when deploying and scaling SpaCy within their own infrastructure.

In summary, Amazon Comprehend provides pre-trained models, seamless integration with other AWS services, and domain-specific features, making it a suitable choice for users preferring an out-of-the-box NLP solution. SpaCy, being an open-source library with more customization options, is a better fit for users who require flexibility in training models, working with specific domains, and having control over their own infrastructure.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon Comprehend
Pros of SpaCy
    Be the first to leave a pro
    • 12
      Speed
    • 2
      No vendor lock-in

    Sign up to add or upvote prosMake informed product decisions

    Cons of Amazon Comprehend
    Cons of SpaCy
    • 2
      Multi-lingual
    • 1
      Requires creating a training set and managing training

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is Amazon Comprehend?

    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications.

    What is SpaCy?

    It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Amazon Comprehend and SpaCy as a desired skillset
    What companies use Amazon Comprehend?
    What companies use SpaCy?
    See which teams inside your own company are using Amazon Comprehend or SpaCy.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Amazon Comprehend?
    What tools integrate with SpaCy?
    What are some alternatives to Amazon Comprehend and SpaCy?
    IBM Watson
    It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.
    Transformers
    It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
    rasa NLU
    rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
    Gensim
    It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
    Google Cloud Natural Language API
    You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.
    See all alternatives