Updated: Dec 17, 2021
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In this article, we are going to see how we can easily integrate a Keyword Extraction engine in your project and how to choose and access the right engine according to your data.
Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that automatically extracts the most used and most important words and expressions from a text. It helps summarize the content of texts and recognize the main topics discussed. Keyword extraction uses machine learning artificial intelligence (AI) with natural language processing (NLP) to break down human language so that it can be understood and analyzed by machines.
In 1999, Turney hypothesized that keywords facilitate a user's reading by allowing him to surf from one key point to another when they highlighted in a text. Other researchers use their synthetic virtues in automatic summary construction methods, but keyword extraction is becoming increasingly useful with the rise of the Internet.
In the 2010s, many researchers are interested in automatic keyword extraction and some evaluation campaigns, such as DEFT and SemEval, propose automatic keyword extraction tasks in order to compare the different existing systems. For this purpose, the data and the evaluation method are the same for all systems. Supervised and unsupervised methods are emerging, and are nowadays combined to train keyword extraction engines.
Top 10 Speech-to-Text API:
Microsoft Azure - Available on Eden AI
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Available on Eden AI
ParallelDots provides Komprehend AI APIs that are a comprehensive set of document classification and NLP APIs for software developers. Their NLP models are trained on more than a billion documents and provide state-of-the-art accuracy on most common NLP use-cases such as named entity recognition, sentiment analysis and emotion detection.
Yonder Labs is a data science company with a special expertise in Natural Language Processing, Machine Learning, and Multimedia Analysis. Yonder is currently releasing new API for extracting semantic information both from single text documents, such as sentiment analysis, entity extraction, semantic tagging, etc. and from collections of texts, allowing for services such as text comparison, clustering, and data mining on text collections.
TextRazor offers a complete cloud or self-hosted text analysis infrastructure. They combine state-of-the-art natural language processing techniques with a comprehensive knowledgebase of real-life facts to help rapidly extract the value from your documents, tweets or web pages. They provide features such as entity extraction, disambiguation and linking, keyphrase extraction, automatic topic tagging and classification.
Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. Amazon Comprehend processes any text file in UTF-8 format, and semi-structured documents, like PDF and Word documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.
Available on Eden AI
Cortical.io provides natural language understanding (NLU) solutions that enable large enterprises to automate the extraction, monitoring, and analysis of key information from any kind of text data. Cortical.io offers AI-based natural language understanding solutions built on technology inspired by Neuroscience.