How Canary Mail, our AI email client uses NLP to understand your emails and helps you writing emails, replies and follow-up reminders
Computers have never been smarter than they are right now, thanks to recent explosive advancements in artificial intelligence. A major game-changer in these advancements has been the use of Natural Language Processing, a tool for computers to analyze and synthesize natural human language and speech.
NLP is increasingly used in everyday tech to make our lives easier and easier. One of the ways you can reap the benefits of using NLP to improve your workflow and personal life is by using Canary Mail. Canary is an AI email client that uses NLP to understand, process and even write your emails like it’s a personal assistant, or as we call to call it, a copilot!
So how can an email client understand and help write your emails? For that, a dive into natural language is necessary.
Natural language processing (NLP) is a branch of artificial intelligence (AI) that focuses on making computers understand human language and speech patterns. It is concerned with the ability of computers to interact with humans by understanding, interpreting, and generating natural languages, such as English, Spanish, or Chinese.
NLP combines aspects of computer science, linguistics, and other related fields to enable computers to process, analyze, and generate natural language text and speech. It involves several tasks, including text processing, applying grammar, analyzing sentiment, and machine translation.
Natural language has numerous applications and is an everyday part of life. NLP enables customer service chatbots, language translation software, speech recognition systems, text-to-speech systems, and more. It is a rapidly evolving field. Recent advances in deep learning and neural networks, such as Chat-GPT, have enabled computers to perform increasingly sophisticated language tasks, such as answering questions and generating coherent, natural-sounding text.
Natural language processing uses algorithms and statistical models to analyze and understand human language. Here are the basic steps for a computer program to understand natural languages.
The first step is lexical analysis:
Lexical analysis includes tokenization, which breaks down text into smaller units, such as words or sentences. This is usually done by using whitespace or punctuation as delimiters or separators. It also includes stemming and POS tagging: Stemming refers to reducing each word to its stem or root word that affixes to suffixes and prefixes. POS (part of speech) tagging involves assigning a grammatical label to each word in a sentence, such as a noun, verb, adjective, or adverb. This helps the computer understand the role of each word in the sentence.
The second step is syntactic analysis:
This involves parsing, which refers to analyzing the grammatical structure of a sentence to understand its meaning. The parsing step can involve identifying subject-verb-object relationships or dependencies between different sentence components. This step also includes named entity recognition (NER), which identifies and classifies named entities in a text, such as people, organizations, and locations.
The third step is sentiment analysis:
Sentiment or discourse is used to uncover the motivation behind a text. It involves determining the emotional tone of a piece of text, such as positive, negative, or neutral.
The last steps are machine translation and text generation:
Translation in this process refers to translating text from one language to another using statistical models or neural networks. Text generation involves generating coherent and natural-sounding text, such as in chatbots or automated news articles.
NLP involves statistical and machine-learning techniques to analyze and understand human language. By breaking down language into constituent parts and applying algorithms, computers can analyze, process, and generate natural language text and speech.
Canary Mail is an email client using natural language processing (NLP) to help users search, organize, and respond to emails. Here are a few examples of how Canary Mail uses NLP:
Natural Language Search:
Canary Mail uses NLP to improve its search functionality. Instead of relying solely on keywords, it can understand the meaning of a search query and return more accurate results. For example, if you search for “emails from Alice January,” Canary Mail can use NLP to understand that you’re looking for emails from Alice that were sent in January.
Canary Mail uses NLP to suggest smart replies, which are pre-written responses that you can use to respond quickly to emails. These responses are generated based on the content of the email you received, and they can save you time when you need to respond to a large volume of emails.
Canary Mail uses NLP to detect which emails you need to delete automatically. It can understand the content of your emails to help weed out which of them are newsletters, spam marketing emails etc., and automatically get selected so you can delete them with one click. If you receive many emails from a specific sender that you do not read or interact with, Canary Mail lets you select all those emails to be deleted. This makes cleaning out and managing emails easier without manually sorting through your inbox.
Canary Mail uses NLP to recognize when an email requires a follow-up and will suggest reminders to ensure you don’t forget. For example, suppose you receive an email asking you to schedule a meeting. In that case, Canary Mail can recognize this and suggest setting a reminder to follow up with the sender if you have yet to respond within a certain timeframe.
Canary Mail uses NLP to help you create email templates. By analyzing the content of your emails, Canary suggests pre-written templates you can customize and reuse in the future. This can save time and effort when writing similar emails repeatedly.
Overall, Canary Mail uses NLP to help users manage their emails more efficiently. By automatically categorizing emails, suggesting quick replies, and recognizing follow-up actions, Canary Mail streamlines the email management process. Using natural language search and email templates also saves users time and effort when composing and searching for emails.
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