What does NLP stand for in AI. Machine learning has ushered us into an… | by Picklai | Feb, 2022 | Medium
Feb 23
Machine learning has ushered us into an era where spam filters, translation tools and virtual assistants have become a reality. These have helped big tech companies expand outside their initial markets and are crucial in the increasingly global human civilization. Natural language processing, abbreviated as NLP, has served as a bedrock for these innovations.
This blog intends to answer:
Read on to know more!
Natural language processing refers to the ability of computers to understand “natural language” (text and speech) the way humans do. As a subfield, it borrows from and lies at the intersection of disciplines like linguistics, computer science and artificial intelligence.
Translation of text from one language to another, responding to textual/spoken commands and summarizing large volumes of text. It holds relevance in domains like research, medicine, business intelligence, search engines etc.
Until the 1990s, natural language processing was largely symbolic. It pertained more to linguistics than technology and was associated with the complex rules of language. This changed with the introduction of statistical modeling, where features/weights, probability and decision trees enabled the discipline to modernize.
The difference between the two eras is summarized by their behavior in face of unfamiliar input. Earlier designs would fail altogether; encountering an exception necessitated addition of new rules. The latter were more robust since they inherently relied on an element of chance and various subtasks.
Since the 2010s, the neural turn has marked a shift to dropping elaborate feature engineering and utilizing neural networks and deep learning. The change is so pronounced that some researchers consider it to be a different field of study.
The first step for an ML algorithm is to obtain inputs. Speech (from microphones), text (typed in or collected from World Wide Web) and images (that have text) are used as inputs. In the first phase, data preprocessing is performed. This is followed by processing of data.
This is an alias for cleaning the data and making it machine-workable by using the following techniques:
For images, optical character recognition is used whereas for spoken input, speech recognition and segmentation is required.
We can go with the rules-based system (used mostly till 1990s, on the basis of linguistic rules) or ML based-system, which uses statistical methods. The latter includes the two following components.
The syntax of the text is assessed for deciphering the meaning. It includes:
The context of individual words is recognized. This encompasses, among various other tasks:
Natural language processing has various use cases.
Further, it also finds application in designing chatbots, text-to-speech engines etc.
Natural language processing, just like artificial intelligence and machine learning, is witnessing boundless innovation every day. The industry demand for the subject experts, who are known as NLP practitioners, will increase too, with the global market expected to breach the $30.48 billion by 2025.
Therefore, if you want to be a part of this revolution, you’d need exemplary machine learning skills and strong data science fundamentals. To start your dream career, log on to Pickl.ai today!
