Diving in at the Deep Learning Summit in San Francisco

[fa icon="calendar'] Jan 23, 2019 7:00:00 PM / by Ana Moreno posted in Chatbots, Deep Learning

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There is only one day left for the Deep Learning Summit in San Francisco and you cannot attend this year? No worries, we’ll be there for you! This year, there are up to 10 different stages where various topics related to AI will come to the front. Stay tuned!

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How To Avoid Misunderstandings with an Effective Anonymization Tool

[fa icon="calendar'] Jan 11, 2019 4:52:13 PM / by Ana Moreno posted in Entity extraction, Anonymization, GDPR

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Has your name or surname ever been mocked? In many languages, people are really embarrassed to have unusual names that make other people laugh. Some names and surnames are treated even as insulting or offensive in some cases. 

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Smarter AI to Identify Languages: When Scripts Are Not Enough

[fa icon="calendar'] Dec 29, 2018 8:00:00 PM / by Ana Moreno posted in AI, Multilanguage, Language Identification

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Did you hear that many people were banned at Twitter just by typing in Cyrillic? The reason was that thousands of Russian bots sent plenty of tweets in the two days preceding the EU referendum. It’s true that Russian language uses letters from the Cyrillic script, but the same is true for more than 20 languages around the world!

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Main Challenges for Word Embeddings: Part II

[fa icon="calendar'] Dec 28, 2018 10:32:29 AM / by Ana Moreno posted in NLP, POS tagging, Phrase Extraction, Deep Linguistic Analysis

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A ‘word embeddings’ approach has been widely adopted for machine learning processes. While an extensive research has been carried out during these years to analyze all theoretical underpinnings of algorithms such as word2vec, GloVe or fastText, it is surprising that little has been done, in turn, to solve some of the more complex linguistic issues raised when getting down to business.

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Main Challenges for Word Embeddings: Part I

[fa icon="calendar'] Dec 28, 2018 10:27:48 AM / by Ana Moreno posted in NLP, POS tagging, Phrase Extraction, Deep Linguistic Analysis

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Machine learning algorithms require a great amount of numeric data to work properly. Real people, however, do not speak to bots using numbers, they communicate through the natural language. That’s the main reason why chatbot developers need to convert all these words into digits so that those virtual assistants can understand what users are saying. And here is where word embeddings come into play.

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