AI Engines
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Clickbait Detection
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The Clickbait detection engine determines the likelyhood of a news article to be written in a “clickbait” style: designed to attract attention and to entice users to follow that link and read, view, or listen to the linked piece of online content, being typically deceptive, sensationalized, or otherwise misleading. The […]
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Speech Transcription
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Automated Speech Transcription automatically processes real-time or recorded audio streams into editable text. Supported languages: Romanian, 50 other languages supported, including cross-language content: Afrikaans (afr), Arabic (ara), Chinese (zho), Croatian (hrv), Czech (ces), Danish (dan), Dutch (nld), English (eng), Estonian (est), Finnish (fin), French (fra), Welsh (glg), German (deu), Greek […]
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Automated Translation
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Automatically Translate text and documents from one language to another, with automatic identification of the source language. Supported target languages: afr (Afrikaans), amh (Amharic), ara (Arabic), ast (Asturian), aze (Azerbaijani), bak (Bashkir), bel (Belarusian), bul (Bulgarian), ben (Bengali), bre (Breton), bos (Bosnian), cat (Catalan), ceb (Cebuano), ces (Czech), cym (Welsh), […]
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Optical Character Recognition
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Transcribes text out of a given image, scan (.jpg, .png, .tif) or PDF file using a text recognition model (Computer Vision). Supported Languages We currently support the following languages: English, Romanian, German, Italian, Arabic, Catalan, Danish, Greek, French, Dutch, Japanese, Polish,Spanish, Portuguese, Russian, Farsi, Macedonian, Lithuanian, Hungarian Usage Example Running […]
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Semantic Comparison
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The Semantic Similarity Engine finds identical meaning contained by the analyzed content pieces, ignoring syntax or grammar. You can even compare content pieces written in different languages. It can be used for clustering documents based on the information they contain. There are two flavors of the Semantic Similarity engine: one […]
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Automatic Summarization
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Understands the meaning of a text by reading only the key sentences. This version uses TF and TF-IDF (term frequency–inverse document frequency) numerical statistics that show how important a word or a combination of words is for a document. Using this approach, each and every sentence and phrase of a […]
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Sentiment Analysis
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Determines the polarity of any text content: negative (neg), neutral (neu), positive (pos). Supported Languages Currently supporting the following languages (14): Arabic, Chinese, English, Farsi, French, German, Hungarian, Italian, Japanese, Polish, Portuguese, Romanian, Russian, Spanish. Usage Example This is an example of calling the Sentiment Analyzer on an English text […]
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Automatic Classification
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Automatic Classifier IPTC Automatic classification of documents using standard IPTC (The International Press Telecommunications Council – the Global Standards Body of the News Media) taxonomy. Classification based on custom taxonomies (patents, cyber security, military intelligence or others) can be created on demand. Taxonomy The out-of-the-box IPTC classifier is trained to […]
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Named Entity Recognition
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Named entities, categorized by type (people, organizations, locations, products, etc.), constitute the core factual information of any content. These engines extract named entities out of given text. All flavors described below are supporting the same list of entity types, see the following section. Supported Entity Types We are currently supporting […]