Synthetic image and video have proven to be a big success for cost-cutting. Synthetic text is following suit: tabular data (that is the data organized in a table with rows and columns) is becoming mainstream already, and the next step is synthetic unstructured text, which is the data that doesn`t have a predefined format.
Synthetic unstructured text supports more complex cases, where actual text in the form of full sentences or documents is required.
Synthetic Training data is the data that is used to train an NLU engine. An NLU engine allows chatbots to understand the intent of user queries. The training data is enriched by data labeling or data annotation, with information about entities, slots…
Arabic is a complex language for NLP tasks, even for simple ones like lemmatization.
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 held 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.)
