When shopping online, customers frequently have the need to modify their order: exchanging an item in the basket, deleting something already added…
In the last COP25 Climate Summit helded in Madrid. Many subjects were 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!
While a lot of research has been devoted to analyzing the theoretical basis of word embeddings, not as much effort has gone towards examining the limitations of using them in production environments
(This article is the first of a series about word embeddings as the basis for user-facing text analysis applications.)
While a lot of research has been devoted to analyzing the theoretical basis of word embeddings, not as much effort has gone towards examining the limitations of using them in production environments
This article is the first of a series about word embeddings as the basis for user-facing text analysis applications.
In previous posts, we have outlined the crucial role of Machine Learning for Analytics (in How to Make Machine Learning more Effective using Linguistic Analysis?), and the implications of using Machine Learning for analyzing and structuring text (in How Phrase Structure helps Machine Learning?). In a following post, we will explain how Linguistics can complement Machine Learning and how it can be integrated in the same technology stack.
This post dives into one of the topics of a previous post "How to Make Machine Learning more effective using Linguistic Analysis". We referred to the strong points of Machine Learning technology for insight extraction. We also stated that text analysis is not the area where machine learning shines the most. Here we go into some detail on this last statement.
