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...
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...
Text analysis is becoming a pervasive task in many business areas. Machine Learning is the most common approach used in text analysis, and is based on statistical and mathematical models. Linguistic approaches, which are based on knowledge of language and its...
Everything looks promising in the world of bots: big players are pushing platforms to build them (Google, Amazon, Facebook, Microsoft, IBM, Apple), large retail companies are adopting them (Starbucks, Domino’s, British Airways), press is excited about movies becoming...
Data scarcity is one of the major bottlenecks for Artificial Intelligence (AI) to reach production levels. The reason is simple: data, or the lack of it, is the number one reason why AI/Natural Language Understanding (NLU) projects fail. So the AI community is working...
When searching for innovative solutions, it is crucial for leaders and decision makers to have the information that allows them to make informed decisions. Bitext is currently at the forefront of technology since it has been mentioned lately in no less than 20...
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