NLP Machine Learning
This article serves as a sneak peek into the elusive and high tech world of NLP Machine Learning. It will give you an idea of what major breakthroughs are happening along with some of the major problems faced in this field.
Once you have a basic intro through this article, you can easily do further relevant research on this topic and start your journey in this cutting edge field.
Reading this article, you will get an idea of:
- What exactly is NLP Machine Learning
- Examples of NLP Machine Learning
- Some projects related to NLP Machine Learning
- NLP Machine Learning Analytics
- How to learn NLP
What is NLP?
Natural Language Processing is a very vital branch of Machine Learning. It provides the most natural medium of communication between man and machine. It deals with the science of making a machine comprehend and analyze human language.
In NLP, the objective is also to potentially make the machine capable of drawing meaningful conclusions after understating human language and then generate a similar language.
One of the most basic examples of NLP in action is Google Search. Nearly every Internet user makes use of this search quite frequently. You just type in a query in your everyday language and Google retrieves the relevant results for you.
NLP Machine Learning Projects
A lot of well-known projects have been done on NLP. You have definitely heard of some of the real-life examples of its implementation. These NLP Machine Learning projects include products like Google Search, Google Translate, Gmail Spam Filters, Auto-Predict in Google Search and Autocorrect for Google keyboard.
Another NLP machine learning example is ‘Rewordify’. It makes great use of text simplification by implementing machine learning algorithms.
In Python programming language, an open-source package called Natural Language Toolkit (NLTK) makes it easier to work on NLP by providing pre-built tools for common NLP tasks.
Amazon Macie is a great implementation of machine learning and NLP in security. It automatically identifies sensitive data and helps secure it. It is a great step up from the traditional DLP (Data Loss Prevention) software.
G-Suite DLP feature also makes use of a similar technology that is now available in the form of a standalone service called Google Cloud DLP API.
SAP is actively implementing machine learning to its business processes like invoice processing, brand recognition and penetration. Customer sentiment analysis is also a major part of this project.
Moreover, predictive analysis is one of the key advances that help businesses stay one step ahead and bring their products early in the market. Reinforcement learning and deep learning have also been successfully applied to solve a variety of different problems.
While on the topic of problems, let us take a look at some of the major issues that are being faced in this area.
Teaching language to computers is a hard job. A lot of work must be done to get meaningful data delivered to organizations and businesses in order to enable machines to comprehend human language.
The first major problem faced by modern AI is disambiguation. Words can have different meanings based on the context and this is difficult to precisely cater using a Boolean structure that is used in machines.
Machines also face difficulty in interpreting slang and sarcasm. A rule-based approach can fail in this case. Implicit meaning is also lost in the rule-based approach used for sentiment analysis.
If we broadly categorize the problems faced in NLP we can come down to these four:
- Understanding natural language
- NLP for low resource situations
- Navigating through large/multiple documents
- Building proper datasets, evaluation procedures.
Surveys indicate that these four issues are currently the most pressing NLP problems and need to be solved in order to make substantial progress in this field.
Modern Machine Learning Algorithms are being developed to counter these known problems. The industry is also actively searching for a viable long-term solution.
NLP Machine Learning Analytics
NLP machine learning analytics is a rapidly growing field. NLP empowers data analysts to turn unstructured text into usable data and insights.
Text Analytics comes in real handy here. It provides structure to the unstructured data, thus enabling it to be analyzed in an easy manner.
Text Analytics starts with Information Extraction. In this process, structured information is automatically extracted from a text data type as a text file. IE can be used to create a new database of information.
An example of using IE in the case of a bank is in a predictive model for determining why customers close their accounts.
Entity resolution and regular expressions are useful when multiple observations point to a common entity like a certain business. Here entity resolution can help clean up the data and provide meaning. It may be done as a pre-processing step or as a text analysis.
An unstructured data clean up is necessary since the data is rarely handed to the analysts in a form that is directly usable. Textual data is therefore cleaned, transformed and enhanced.
In today’s modern age of information, data without analytics can be considered as a wasted opportunity and that’s where NLP steps in.
The importance of Natural Language Processing cannot be stressed enough. It is emerging as one of the most important subfields of machine learning. It has really enabled us to converse more naturally with computers.
Now it’s just up to you to benefit from the endless opportunities offered by this field and to make the most of it by being a part of this exciting process.