One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.
All Machine Learning (ML) engines that work with text can benefit from a solid linguistic background. If they are working in a multilingual environment, the need of a good lexicon (with forms, lemmas and attributes) is overwhelming. Even so, basic features such as Word Embeddings hugely improve when enriched with linguistic knowledge, and if this is not usually applied, is because of a lack of linguists working for ML companies.
The company CB Insights has recently published a document named “Lessons From The Failed Chatbot Revolution”. This ominous title reveals a hard truth: chatbots have not been the revolution we expected.
A few days ago, Amazon Web Services organized AWS re:Invent, one of the world biggest IT events, focusing on everything Amazon has to offer. Among the great amount of novelties that were announced, some of them were very interesting for us.
These days the COP25 Climate Summit is being held in Madrid. Many subjects are being discussed on the matter of a possible climate crisis, and how to face it. Has Machine Learning (ML) and Natural Language Processing (NLP) something to say about it? Surprisingly, yes, it does!
Is this the era of Fake News? We could say that fake news are one of the most prominent (and dangerous) phenomena on social media. Since they are spreading to traditional media as well, having a reliable way of identifying fake news is more relevant than ever.