Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend uses machine learning to help uncover insights and relationships in unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text…
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Amazon Transcribe
Score 7.9 out of 10
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Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately. Amazon Transcribe can be used to transcribe customer service calls, to automate closed captioning and subtitling, and to generate metadata for media assets to create a searchable archive. Amazon Transcribe Medical can be added to provide medical speech to text capabilities to clinical documentation applications.
Specifically, it starts processing millions of documents in minutes by leveraging the power of machine learning without having trained models from scratch. If any of the content contains personally identifiable information not only can Amazon Comprehend locate it but it will also redact or mask it. Using NLP techniques Amazon Comprehend goes well beyond keyword search or rules-based tagging to accurately classify documents. For my task or development, I cannot find any difficulties with Amazon Comprehend.
Amazon Transcribe can be an excellent tool for businesses where being able to convert speech or audio to text, in a searchable and reportable form, would be useful. For a call center (inbound or outbound), the ability to have a rich transcription of each call (and being able to search it for keywords) is an incredibly valuable benefit. For business meetings, being able to turn a 60 or 90-minute call into a readable transcript to search or refresh yourself or others is a very large time saver which will help you work more efficiently. The software does offer many deeper integrations, such as being able to track script usage (for call centers) or interruptions, deviations, etc.. which would be very valuable to a management team and for training purposes.
Amazon Comprehend identifies the language of the text and extracts Key-phrases, places, people, brands or events.
It can build a custom set of entities or text classification models that are tailored uniquely to the organisation's need
Amazon Comprehend's medical can be used to identify medical conditions, medications, dosages, strength and frequencies from sources like doctor's notes, clinical trial reports and patient health records. This service is very good and with well an accuracy or confidence score.
There is a small learning curve to begin using ALL of the features the software offers. Additional tech support may be required for some integrations, so it's worth looking into if planning to use all of the features they offer.
For natural language processing tasks or techniques, there are many service providers out there in the market such as Azure Cloud Services, IBM Watson and Google Cloud Platform (GCP), but compared with them, Amazon Comprehend is the best service provider in contents of accuracy, speed of processing multilingual text, supporting SDK for most of the languages and well documented.
I use Google Cloud Speech to Text and Amazon Transcribe. What makes Amazon Transcribe better for me is the accuracy of the audio-to-text conversion. I have found out that Amazone Transcribe is better at handling homophones, contractions, abbreviations, and acronyms. Another feature that makes Amazon Transcribe my No. 1 choice is its use of punctuation marks. I can also feed my own list of vocabulary into Amazon Transcribe to help me acquire better results.
It supports better and accurately as compared with our existing or old implementations. So, we fulfil our needs as per clients' requirements and it will help to grow or improve client satisfaction.
For these specific requirements, we do not require any machine learning engineers or related professionals to hire in our organisation.
None of any negative sides can be affected our business or distract existing clients.
Working in the backend, I would say the most important ROI has been data security through implementation of enterprise-grade technical and physical controls which prevent unauthorized access to our content.
