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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AI Challenges Are Being Met but... Who Broke Ground Here?

[fa icon="calendar'] Dec 13, 2018 3:39:54 PM / by Ana Moreno posted in Query Rewriting, NLU, Deep Linguistic Analysis

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While AI is one of the most important trends nowadays, there are still challenges to overcome. Apart from common technical issues such as a lack of quality data, there is much  beyond its abilities for an AI to effectively understand and react when it comes to human-machine interaction.

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Google I/O: Multiple Actions or Double Intent?

[fa icon="calendar'] May 24, 2018 5:40:07 PM / by Leticia Martín-Fuertes posted in NLU, Deep Linguistic Analysis

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In the past weeks a lot has been said about last Google I/O's presentation of Duplex, an assistant powered by AI that can make phone calls and talk to humans to make arrangements for you. Some people are so impressed by the achievement that they are already pointing out the ethical consequences of not being able to tell apart a human from a machine, and some are playing it down, highlighting that we have only seen a demo.

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Using Word2Vec for ontology creation

[fa icon="calendar'] Aug 18, 2017 12:30:00 PM / by Mario Maged Mina posted in Deep Linguistic Analysis

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An ontology is a data structure that groups entities in domains or types (for example the entities ‘dog’ and ‘cat’ are grouped under the type ‘animals’), and establishes relations between those entities. Its uses in Computational Linguistics are vast, one of the most interesting for us is the application of ontologies for chatbot training. When two humans communicate, they have a shared knowledge of the world that they presume in any spoken interaction. However, a chatbot lacks this indispensable knowledge. An ontology can help the chatbot discern that a person can walk a dog, but a dog walking a person is not something possible in our world.

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