Natural Language Processing (NLP) is a method that prepares computers in understanding and accurately translating human language. The representation of human language is built through the building of computational algorithms, enabled in a wide range of applications including Google and Amazon, two of the most powerful and popular companies.
The ability to translate human language into text isn’t exactly a method of machine learning but can be considered as a method that prepares a machine to perform a language illustration. For example, suppose a user in google translator writes some text in English and selects to translate into Spanish. The back-end process will involve the execution of translating the text, with the machine delivering the outputted result, such as Alexa, the machine that voice outputs the translation. Machines cannot fully understand the human text to perfection, but the progression of training machines through algorithms allows for training them to deliver certain tasks. Machines are now being taught to conduct a simple conversation with a human being.
The interpretation of NLP output is derived through two methods; Unsupervised Learning and Supervised Learning. Let’s take a look at both in detail.
Supervised Machine Learning for NLP
The term supervised machine learning is the process of understanding or learning a function from a training dataset, or a pre-defined input and output, and to deliver that intended input and output. The input and output are already determined, and the ML system needs to interpret that exactly as documented. The algorithm learns the dataset or the teacher as a more understandable term. The goal is to ensure that the output is accurate enough to be predicted from the input. The algorithm goes through an iterative training phase where data is fed back until it achieves the highest level of accuracy. Some of the common supervising methods in machine learning natural language processing include Linear Regression, Artificial Neural Networks (ANN), Random Forests and Supper Vector Machines.
Unsupervised Machine Learning for NLP
The term unsupervised machine learning is the process of learning the input from a dataset, where the algorithm is given free rein to construct a reasonable output. As part of the dataset, the algorithm does not receive the intended output and is, therefore, functioning on its own to predict appropriate findings by studying the input. Unsupervised learning is popular for clustering applications or randomized groups of data, where the ML is expected to make sense of the data formed from learning the input. Basically, it is not guided or supervised by an output. Other applications that are unsupervised include Anomaly Detection, Visualization, and Dimensionality Reduction.
Both supervised and supervised machine learning methods have benefits and threats in their own right. For example, supervised learning may have difficulty with new sources of information. Below detail some comparisons on what both supervised and unsupervised machine learning methods are used for:
|
Task |
Supervised Learning |
Unsupervised Learning |
|
Real-Time |
Uses predefined data |
Uses Real-Time or live data |
|
Results Accuracy |
Acutely Accurate and Reliable results |
Moderate accuracy with some inconsistencies |
|
Data Input |
Data known through labels and tags |
Data sources unknown |
The banking industry is purchasing more NLP-based products, with at least a quarter of AI approaches catering to information retrieval functionalities This is more catered to document searching capabilities. According to Deltec Bank, “Banks see this as moving into the direction of meeting auto compliance so that key information can be retrieved amongst a process of analyzing and output.” Key information such as legal, regulation and risk information are considered key compliance information that banking and finance see NLP processing as beneficial for. Chatbots and NLP can work hand in hand together, where a human requests certain information, with NLP searching for the information through documents before outputting the answer through chatbots in real-time JPMorgan has launched COIN, there in-house software designed to manage legal documents through NLP.
To sum up, NLP continues to hold huge interest amongst banking and finance institutions to manage the translation of the human text. Banking and Finance would be expected to implement supervised learning for the analysis and output of digital documents, and the same for customer inquiries. As we now know, institutions are expected to follow suit soon to help reduce resource, administration, and costs.
Disclaimer: The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.
Media Contact
Company Name: Deltec International Group
Contact Person: Media Manager
Email: Send Email
Phone: 242 302 4100
Country: Bahamas
Website: https://www.deltecbank.com/