The first major leap forward for natural language processing algorithm came in 2013 with the introduction of Word2Vec – a neural network based model used exclusively for producing embeddings.

Algorithms that fall under the label “ natural language processing (NLP) ” are deployed to roles in industry and homes. They’re now reliable enough to be a regular part of customer service ...

Deep-Transfer-Learning–Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer The first approach led to a ...

The utility of natural language processing algorithms for understanding and predicting the changes of viruses lies in the adherence of viral structures to a kind of grammar and semantics, MIT ...

Accern Corp., a natural language processing startup focused on the financial services sector, today announced that it has closed a $20 million funding round co-led by Mighty Capital and Fusion Fund.

We’ll break that down further in the next section. Historically, natural language processing was handled by rule-based systems, initially by writing rules for, e.g., grammars and stemming.

Google fine-tuned its BERT algorithm update on natural language processing tasks, such as question and answering, to help it understand the linguistic nuances of a searcher’s query.

NLP algorithms can scan vast amounts of social media data, flagging relevant conversations or posts. These might include coded language, threats or the discussion of hacking methods.

2- A natural language processing (NLP) library: Choose an NLP library that can help your system understand the intent behind the user's voice commands.