Stemming and lemmatization are methods used by search engines and chatbots to analyze the meaning behind a word. Stemming uses the stem of the word, while lemmatization uses the context in which the word is being used. We'll later go into more detailed explanations and examples.  

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.

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Powered by a linguistic approach, the future of natural language processing will enable human-like understanding through a wide range of applications. Thanks to Bitext NLP technologies, that far future is closer than expected. Bitext solutions are fully oriented to the current needs of many forward-looking companies relying on cutting-edge techniques ranging from sentiment analysis tools to a generation of artificial training data. After years of hard work in the field of the automation of customer support, Bitext developed the most advanced API ever seen and several conversational agents for innovative enterprises as, for instance, TechCrunch.