Natural Language Services in Azure Cloud

NLP Services in Azure

Natural Language Processing (NLP) in AI involves understanding written and spoken language, as seen in voice assistants like Cortana, Siri, or Alexa. NLP includes tasks like speech-to-text, text-to-speech, sentiment analysis, and entity detection in text. It also enables translation between languages and interprets commands for actions. This cognitive aspect, known as language understanding, is crucial for applications like voice assistants.

Unlike Computer Vision, there's no service just called natural language processing. Azure breaks it down into 4 main services.

Azure Services For NLP

• Text Analytics service: 

• Here we can get back results on entities, sentiments, etc.
• Text Analytics involves evaluating document or sentence content, determining language, sentiment analysis, keyphrase extraction, and entity recognition. 
• It helps assess text aspects, such as language dominance and sentiment (positive/negative). 
• The service extracts key phrases, aiding in summarization, and recognizes entities like people, dates, and locations with confidence scores. 
• Azure offers standalone Text Analytics or integration with Cognitive Services. 

• Azure Speech: 

• We can use this service to recognize and synthesize speech. It will also allow us to translate spoken languages.
• Azure Speech enables speech recognition, converting spoken language into text or data using the speech-to-text API. 
• This functionality is useful for real-time transcriptions or batch transcriptions from audio files. 
• On the flip side, speech synthesis transforms text into speech, applicable for voice menus, reading emails aloud, or broadcasting announcements. 
• The text-to-speech API facilitates this conversion, allowing the resulting computer voice to be played directly or saved as an audio file. 
• Azure provides a standalone Speech service for focused usage tracking or integration with the broader Cognitive Services for a unified endpoint and key.

• Translator Text Service: 

• It does exactly what it sounds like and will allow us to translate text between more than 60 languages.
• Text translation on Azure involves translating documents or texts from one language to another, similar to Google Translate. 
• The service supports over 60 languages for text-to-text translation, allowing users to specify the source and target languages. 
• Options like the profanity filter and content tagging enhance translation accuracy. 
• Speech translation capabilities include direct speech-to-speech translation for presentations and translating spoken language into text files. 
• Azure offers the Translator text service for text translation and Azure Speech for comprehensive speech translation, both falling under the Cognitive Services umbrella. 

• Language Understanding Intelligence Service / or LUIS for short: 

• This is where we'll be able to train a language model that can understand us and our intents.
• Language understanding involves enabling computers to comprehend and respond to spoken phrases, as seen in home assistants like Cortana. 
• This process breaks down into three key elements: 
• Utterances (spoken phrases).
• Entities (items referred to in the phrases). 
• Intents (the purpose or goal of the phrases). 
• For example, in the phrase "Start the coffee machine," "coffee machine" is the entity, and "start" is the intent.

Azure provides the Language Understanding standalone service for authoring (training the model with entities, intents, and utterances) and prediction (deploying the trained model). It can be used independently or under the Cognitive Services umbrella for a unified endpoint and key. Notably, Language Understanding is specifically designed for predictions.

To succeed in using Language Understanding, one needs to grasp the distinctions between utterances, intents, and entities.

• That utterances are spoken phrases
• Intents are our goal or what we're trying to accomplish,
• Entities are the items