Data Science at DIT: harnessing the potential of Natural Language Processing

Applications like GPT-3, GPT-4, and Google Brain are taking NLP to a futuristic level known as natural language generation. While the likes of Alexa, OK Google, Siri, and Cortana are advanced NLP models, this new breed of technology is taking us to a new era of understanding language. The problem with Alexa or Siri is that you have to find apps to solve problems manually, and it returns you will get a cue card type response. GPT-3 uses real https://www.metadialog.com/ context clues to solve the problem of filling in the language gaps. Although much of the article is about word correlation rather than a genuine understanding of language and context, it was a big breakthrough in terms of applications of natural language processing. Although NLP technology is far from reaching full maturity, some of the most cutting-edge applications of natural language processing show that a new stage of AI is upon us.

NLP machines commonly compartmentalize sentences into individual words, but some separate words into characters (e.g., h, i, g, h, e, r) and subwords (e.g., high, er). Natural language generation refers to an NLP model producing meaningful text outputs after internalizing some input. For example, a chatbot replying to a customer inquiry regarding a shop’s opening hours. An important but often neglected aspect of NLP is generating an accurate and reliable response.

The bottom line: Text mining vs. NLP

The combination of predictive coding, machine learning embedded and natural language processing can also be used by lawyers to understand better the likelihood of how a court or judge may rule. A case in point is a study conducted in 2016 that discovered that machine learning and natural language processing could predict how the European Court of Human Rights would decide on a case with 79% accuracy [11]. This is a major benefit to lawyers as understanding the history and identifying a pattern in a court’s ruling can assist lawyers in tailoring their arguments to support or go against a prediction [12]. Key pieces of information identified regarding previous rulings, the judge’s thinking process and any common facts can hugely impact the route a lawyer takes to structure their argument and win a case. Natural language processing in a chat interface allows chatbots and digital assistants to answer questions using natural human language and communicate with clients. Popular digital assistants like Alexa and Siri are great examples of how natural language processing is used in everyday life.

The goal of NLP is to create software that understands language as well as we do. Natural language processing (NLP) is a branch of artificial intelligence (AI) that assists in the process of programming computers/computer examples of natural language processing software to ‘learn’ human languages. There is now an entire ecosystem of providers delivering pretrained deep learning models that are trained on different combinations of languages, datasets, and pretraining tasks.

Data Cleaning in NLP

Traditionally, companies would hire employees who can speak a single language for easier collaboration. However, in doing so, companies also miss out on qualified talents simply because they do not share the same native language. The entity linking process is also composed of several two subprocesses, two of them being named entity recognition and named entity disambiguation. By making your content more inclusive, you can tap into neglected market share and improve your organization’s reach, sales, and SEO.