The Keyword Extraction API enables users to extract essential keywords or phrases from a given text or document. Once a piece of text is inputted, the API generates a list of keywords that are considered the most relevant or representative of the content.
To identify and rank the most important keywords, Keyword Extraction APIs commonly utilize natural language processing (NLP) techniques and machine learning algorithms. These algorithms may consider factors like the frequency of terms, relevance of terms, contextual information and statistical patterns to determine the significance of each keyword.
This technology enables users to automate the process of identifying key concepts within a large volume of text, thereby enabling them to analyze and organize textual data more effectively.
For users seeking a cost-effective engine, opting for an open-source model is the recommended choice. Here is the list of the best Keyword Extraction Open Source Models:
YAKE is a simple yet effective method for automatically extracting keywords from a single document, using statistical features to identify the most important terms. Their system does not require specific document training, nor does it rely on dictionaries, external corpora, text size, language, or domain.
KeyBERT is a simple and user-friendly method for extracting keywords that utilize BERT embeddings to generate the most relevant terms and phrases in relation to a document.
Python Keyphrase Extraction (pke) is a Python-based tool for extracting keyphrases from text, available as open source. Its end-to-end pipeline for extraction of keyphrases can be customized easily for developing new models.
RAKE, a tool for Rapid Automatic Keyword Extraction, is a method of determining key phrases in a text by examining the frequency and their co-occurrence. It is not limited to specific subject areas.
This is a python implementation of TextRank for automatic keyword and sentence extraction (summarization).
Gensim is a Python library for topic modeling and document similarity analysis. It includes functions for keyword extraction using algorithms like TF-IDF.
While open source models offer many advantages, they also come with some potential drawbacks and challenges. Here are some cons of using open source models:
Given the potential costs and challenges related to open-source models, one cost-effective solution is to use APIs. Eden AI smoothens the incorporation and implementation of AI technologies with its API, connecting to multiple AI engines.
Eden AI presents a broad range of AI APIs on its platform, customized to suit your specific needs and financial limitations. These technologies include data parsing, language identification, sentiment analysis, logo recognition, question answering, data anonymization, speech recognition, and numerous other capabilities.
To get started, we offer free $10 credits for you to explore our APIs.
Our standardized API enables you to integrate Keyword Extraction APIs into your system with ease by utilizing various providers on Eden AI. Here is the list (in alphabetical order):
AWS's API is a dependable and efficient option with scalable infrastructure. Advanced customization features offered by AWS enable users to refine the keyword extraction model for their precise requirements. AWS facilitates seamless integration with other AWS services, empowering a comprehensive and unified data processing pipeline.
The Emvista API utilizes cutting-edge NLP methods and machine learning algorithms to scrutinize text and recognize critical keywords. Emvista offers a Verbatim Analysis solution that presents a well-organized list of the most substantial keywords within a specified context. This potent tool enables users to effortlessly ascertain the keywords with utmost significance.
Drawing on IBM's extensive proficiency in AI and machine learning, the API proficiently pinpoints and prioritises the most pertinent keywords in a given text, regardless of language or domain. With sophisticated NLP algorithms, IBM guarantees consistently precise results for keyword research output.
With the use of Azure's keyword extraction API, extracting crucial keywords and phrases from text documents has become uncomplicated. This allows for more efficient information retrieval and analysis. The API's accuracy and robustness stem from Microsoft's significant expertise in language understanding and NLP research.
NLP Cloud utilizes state-of-the-art NLP algorithms to guarantee precise identification of keywords, even in intricate and diverse language contexts. Furthermore, NLP Cloud extends comprehensive language support, rendering it appropriate for multilingual applications.
Leveraging cutting-edge language models such as GPT-3.5, OpenAI achieves precise identification of keywords with contextual comprehension. With its capability of accommodating various languages and thorough training on vast datasets, OpenAI's API provides unparalleled proficiency and versatility for effective keyword extraction.
Tenstorrent is able to precisely recognize pertinent keywords and eliminate any extraneous information through its sophisticated machine learning algorithms. The API offers users customization options to refine the keyword extraction model based on their exact needs, which guarantees precise and tailored outcomes.
Eden AI offers a user-friendly platform for evaluating pricing information from diverse API providers and monitoring price changes over time. As a result, keeping up-to-date with the latest pricing is crucial. The pricing chart below outlines the rates for smaller quantities for November 2023, as well as you can get discounts for potentially large volumes.
Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.
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The Eden AI team can help you with your Keyword Extraction integration project. This can be done by :