Our platform gives access to a large number of text analysis and natural language processing engines from the best providers.
Emotion Detection is a natural language processing technique that analyzes written text to categorize the emotional content from joy to sadness and more.
Entity sentiment analysis evaluates the emotional tone of specific entities in text, offering insights into whether they are described positively, negatively, or neutrally.
Plagiarism detection is the automatic detection of instances when text or intellectual property has been copied without due credit.
Custom text classification is the process of training machine learning models to categorize strings of text into specific, user-defined labels or categories.
Text Embeddings are numerical representations of text where each word or phrase is represented as a dense vector of real numbers. They are used to capture the underlying meaning and semantic relationships between words in a text corpus.
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Prompt optimization involves refining input instructions for AI models to achieve more accurate and relevant outputs, enhancing the quality of generated responses.
Custom entity extraction, also known as custom Named Entity Recognition (NER), is an NLP technique to identify and classify specific entities in text. Unlike traditional NER, it creates personalized models for unique entities.
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AI Content Detectors uses artificial intelligence algorithms to determine whether a given text was generated by an artificial intelligence chatbot or a Language Model (LLM).
Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that consists of automatically extracting the most important words and expressions in a text or a document.
Text moderation scans text for offensive, sexual, explicit or suggestive content to ensure that it adheres to certain guidelines or policies.
Sentiment Analysis allows you to extract emotions and feelings in a given string of text. Also called Opinion Mining, it uses Natural Language Processing (NLP), text analysis and computational linguistics to identify and detect subjective information from the input text.
NER (also called entity identification or entity extraction) is an information extraction technique that automatically identifies named entities in a text (places, people, brands, and events) and classifies them into predefined categories.
Language Detection automatically defines the most likely language in which a text or document is expressed.
Summarization is the process of reducing a text to a shorter form while keeping the most important information.
Grammar and spell check technologies are designed to help writers improve the quality of their written work by automatically detecting and correcting misspelled words, errors in grammar, punctuation, and syntax.
Question Answering generates an answer to a question based on a set of documents. This is useful for question-answering applications on sources of truth, like company documentation or a knowledge base.
Syntax Analysis (also called Parsing) is used to carry out a syntax analysis of a given text to reveal the syntactic components and their grammatical relationships.
Text Anonymization's intent is privacy protection. It is the process of removing personally identifiable information from text so that the people described by the data remain anonymous.
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Topic extraction is a natural language processing technique that automatically identifies and extracts the main topics from a piece of text.
"As new AI services hit the economy and the various competitors vie for the top spots in terms of quality and pricing, you can easily switch between APIs and vendors without disrupting your infrastructure or duplicating work. I use it with Make and they are a low-code match made in heaven."
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Leandrew. D
Founder of ParrotDown
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