Artificial intelligence (AI) and its potential for the future is uncanny. One of the most significant innovations of AI technology is natural language processing in order to understand user queries and commands. The advancements in Natural Language processing, as well as its modern applications, are astounding.
Here, we’ll explore advancements in this technology as well as what’s on the horizon for NLP.
Advancements in Natural Language Processing
Natural Language Processing is on the rise. Using Python and other coding languages, Natural Language Processing has the ability to contextualize, understand, and translate human language in three key areas. These are:
1) Linguistics
Data has driven the linguistic understanding of NLPs in recent years. With systems that detect and analyze language based on morphology, syntax, semantics, and pragmatics, the ability of this intelligence to parse out language based on context has come far.
The research team at Virginia Tech analyzed 6,500 reviews of whiskey products using NLP systems in order to help contextualize and analyze how we perceive taste. The implications of this for marketing are huge, as better descriptors help target products and guide customers.
Large data samples can be combed over by AI tech to find commonalities and context in language. This helps improve machine understanding and pick up on patterns we as humans might not even be aware of, in addition to enabling the automation of certain communicative processes.
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2) Translation
Anyone who’s used Google Translate understands that the AI we currently have access to is far from perfect when it comes to translating text from one language to another. Advancements in NLPs hope to change that.
The Stanford Natural Language Processing Group has made significant progress with a system called Minimum Error Rate Training (MERT). This system uses algorithms to detect errors in language processing and optimize output.
While a future in which you can input a long string of text into a free tool like Google Translate and get a comprehensible result may still be a ways off, it’s not too distant. NLP tools like MERT are improving these processes all the time.
3) Transfer Learning
A popular innovation in the NLP field is transfer learning, or taking a pre-trained language model and transferring it into a different environment.
Transfer Learning is especially useful when users don’t have a data set large enough to begin a deep learning process from scratch. A pre-trained model has already learned up to a point, which can then be transferred into a similar environment for better, faster results.
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Applications for the Future
NLP development has taken massive strides in the last few years. In a world ripe with ethical concerns in AI like gender or racial bias, discrimination, and disinformation commonly referred to as ‘fake news,’ the power of NLP to understand and contextualize this information could lead to a future in which we can better flag inaccuracies.
In the future, we can instantly translate texts or teach machines to comprehend languages and commands. There are bright things on the horizon for Natural Language Processing.
Perhaps someday soon, this technology will allow us to live in a world without the kind of barriers created by language differences and miscommunication.
