In a few words, natural language processing, or NLP, is a field of research and application that explores how computers and technology can be employed to understand and manipulate natural human languages in text and speech. In other words, NLP is a subfield of artificial intelligence that’s exclusively focused on empowering computers to read and process human languages and to get computers closer to a human-level understanding of language.

Computers don’t have the equal intuitive understanding of natural language that regular people do. As a result, there’s an enormous difference between the way people communicate with each other and how they communicate with computers. When writing programs, people need to be very careful when using syntax and structure. On the other hand, when people talk to other people, they can take many liberties. For example, they can make short sentences, or they can make longer sentences, can use puns and sarcasm, or they may not.

Having said that, novel advances in machine learning and the rise of NLP machine learning have allowed computers to do many relevant things with natural human languages. For instance, deep learning has allowed us to write programs that deliver language translation, text summarization, semantic understanding, and even machine learning text analysis. Consequently, as artificial intelligence becomes ubiquitous by finding its way into more and more of our everyday tasks and devices, it becomes critical for us to communicate with computers in the language we’re familiar with.  

And NLP machine learning is all about leveraging tools, techniques, and algorithms to process and understand natural language-based data, which usually comes as unstructured text or speech. In this blog post, we’ll be looking at how the basic and advanced concepts of NLP machine learning help solve problems like text summarization, classification, and other human language barriers. But first, we’ll start from the beginning and explain what NLP machine learning is and how it works.

Machine Learning As Part Of Natural Language Processing

Before we dive deeper into applying NLP machine learning, let's clarify some basic ideas about the whole process and technology. First and foremost, machine learning really means machine teaching. As you already know, machines need to learn, so our principal responsibility is to create a learning framework and provide accurately formatted, relevant, clean data for the machines to learn from.

Also, when we speak about a machine learning model, what we're thinking about is a mathematical representation, where proper input is vital. So, a machine learning model is the sum of the learning that has been gained from its training data, where the model transforms as more knowledge is acquired. Contrary to algorithmic programming, machine learning models can generalize and successfully deal with new cases. So, if there's a case resembling something the model has seen previously, the model can use this "prior learning" to evaluate the novel case better. The ultimate goal here is to create a system where the machine learning model steadily improves over time.

NLP machine learning includes a set of statistical techniques for identifying entities, sentiment, parts of speech, and other aspects of a text. These methods can be expressed as an NLP-powered machine learning model applied to other texts, also known as supervised machine learning. Or it could also be a set of algorithms that work across enormous data sets to extract meaning, which is better known as unsupervised machine learning. It doesn't matter if you go with a structured or unstructured NLP machine learning model; both systems can help your team of data analysts turn unstructured text into usable data and insights. Y Meadows' solution embraces NLP machine learning and can be easily integrated within your organization's systems to help computers understand, analyze, manipulate, and potentially generate human language.

Supervised NLP Machine Learning

When we speak about supervised NLP machine learning, a group of text documents is annotated or tagged with examples of what the computer or machine should look for and how it should interpret that aspect. These docs are later used to train a statistical model, which is afterward given untagged text to analyze. After that, you can use more extensive and better data sets to retrain the model as it learns more about the docs it examines along the way. As an example, you can use supervised NLP machine learning to train a model to dive deep and analyze the customer reviews and then later train it to factor in the reviewer’s star rating.

The most popular supervised NLP machine learning algorithms include deep learning/neural networks, support vector machines, maximum entropy, Bayesian networks, and conditional random fields. These terms represent a set of data scientist-guided machine learning algorithms integrated into our NLP-powered solution to help you build and improve core text analytics functions and other features. Here’s how.

Tokenization

Tokenization includes breaking a text document into fragments that a machine can understand, like words. As an English speaker yourself, you’re probably really good at figuring out what’s a word and what’s gibberish because English is straightforward. All the white space in between letters and paragraphs makes the English language really easy to tokenize. Still, how do you train a machine to know what a word looks like? Moreover, what if you’re not working with only English-language documents because logographic languages like Vietnamese have no whitespace between words.

This is where NLP machine learning for tokenizations proves its worth. Vietnamese and any other language out there follow specific rules and patterns just like English, so if we train an NLP-powered machine learning model to identify and understand logographic languages, your customer support team will have no difficulties whatsoever in solving problems for clients that prefer to communicate in their native language with your organization.

Named Entity Recognition

Simply put, named entities are people, products (things), or places mentioned in a text document. Nowadays, entities can also be emails, mailing addresses, phone numbers, and even hashtags. Actually, anything can be an entity if you view it the right way. For that reason, Y Meadows’ NLP machine learning-powered software comes with trained, supervised NLP machine learning models on vast amounts of pre-tagged entities.

Sentiment Analysis

Sentiment analysis is the method of recognizing whether a piece of writing is positive, negative, or neutral and then attaching a weighted sentiment in order to score each topic, theme, entity, and category within the document. This is an unbelievably complicated task that varies greatly within context. For instance, take the phrase “sick burn” in the context of video gaming, as this might be a positive statement after all.

Developing a group of NLP rules to account for each possible sentiment score in every possible context is highly unlikely. Nevertheless, by tutoring a machine learning model on pre-scored data, the computer can learn to recognize what “sick burn” means in the context of video gaming as opposed in the setting of healthcare, for example. Unfortunately, each human language requires its own sentiment classification type.

Classification And Categorization

Classification and categorization suggest classifying content into containers to get an expeditious, high-level summary of what’s in the data. In order to train a text classification and categorization model, data scientists utilize pre-sorted content and gently shepherd their model until it’s reached the desired level of accuracy. The result is accurate, reliable categorization of text documents that take shorter time and energy than human analysis.

Unsupervised NLP Machine Learning

On the other hand, unsupervised NLP machine learning comprises training a model without annotating or pre-tagging. Surprisingly, many of these techniques are pretty easy to understand.

To begin with, clustering is grouping similar documents together into sets or groups. Afterward, these clusters are sorted based on relevancy and importance, which is also known as hierarchical clustering. ‍

Latent semantic indexing, or LSI, identifies words and phrases that commonly occur with each other. Companies worldwide utilize LSI for faceted searches or return search results that aren’t the exact search term. ‍

Matrix factorization uses latent factors to break a large matrix down into two or more smaller matrices, with latent factors being the similarities between the items. For example, let’s take the sentence “Tim threw the football over the fence.” The word threw is way more likely to be associated with football than with fence. As humans have a natural ability to comprehend the factors that make some objects throwable, NLP machine learning algorithms must be taught this difference.

Utilizing Machine Learning On Natural Language Sentences

Well, let’s return to the sentence from above, “Tim threw the football over the fence.” If we separate this sentence, there are three types of information in it:

• Syntax information: subject - action - direct object - indirect object
• Semantic information: person - the act of throwing the football - spherical play item - fence
• Context information: this sentence is about a person named Tim playing with a football
  

Well, this information isn't really helpful by itself. However, they indicate a vague idea of what the sentence is about, but full understanding requires the successful combination of all three components.

This analysis can be arranged in numerous ways, through NLP machine learning models or by inserting rules for a computer to comprehend when dissecting a sentence like this one. NLP machine learning software like Y Meadows’ model is fantastic at recognizing entities and overall sentiment for a document to extract topics and themes and match sentiment to individual themes or entities.

Otherwise, you can teach your system to identify some basic rules and patterns of human language. For instance, in most languages, a proper noun followed by the word “street” probably means a street name. Likewise, a number followed by a proper noun followed by the word “street” is probably a street address. However, this approach of recording and implementing language rules takes a lot of time and human effort. For that reason, we advise you to contact our sales department and find out what our NLP machine learning solution can bring to the table for your business.

Final Thoughts

In summary, human languages are very messy and complex, meaning they vary from speaker to speaker and listener to listener. Nevertheless, NLP-powered machine learning solutions can be fantastic options for analyzing text data and extracting value from it to serve your company’s everyday needs in various departments.