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Machine Learning for Natural Language Processing

Machine learning for natural language processing and text analytics involves using machine learning algorithms and “narrow”

These documents can be just about anything that contains text: social media comments, online reviews, survey responses, even financial, medical, legal and regulatory documents.

The role of machine learning and AI in natural language processing (NLP) and text analytics is to improve, accelerate and automate the underlying text analytics functions and NLP features that turn unstructured text into useable data and insights.

Then we’ll discuss how to apply machine learning to solve problems in natural language processing and text analytics.

Before we dive deep into how to apply machine learning and AI for NLP and text analytics, let’s clarify some basic ideas.

Most importantly, “machine learning” really means “machine teaching.” We know what the machine needs to learn, so our task is to create a learning framework and provide properly-formatted, relevant, clean data for the machine to learn from.

Machine learning for NLP and text analytics involves a set of statistical techniques for identifying parts of speech, entities, sentiment, and other aspects of text.

It also could be a set of algorithms that work across large sets of data to extract meaning, which is known as unsupervised machine learning.

In supervised machine learning, a batch of text documents are tagged or annotated with examples of what the machine should look for and how it should interpret that aspect.

For example, you can use supervised learning to train a model to analyze movie reviews, and then later train it to factor in the reviewer’s star rating.

The most popular supervised NLP machine learning algorithms are: All you really need to know if come across these terms is that they represent a set of machine learning algorithms that are guided along in some way by a human data scientist.

Sentiment analysis is the process of determining whether a piece of writing is positive, negative or neutral, and then assigning a weighted sentiment score to each entity, theme, topic, and category within the document.

But if you train a machine learning model on pre-scored data, it can learn to understand what “sick burn” means in the context of video gaming, versus in the context of healthcare.

To train a text classification model, data scientists use 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 takes far less time and energy than human analysis.

We extract certain important patterns within large sets of text documents to help our models understand the most likely interpretation.

This web allows our text analytics and NLP features to understand that “apple” is close to “fruit” is close to “tree”, is far away from “lion”, but is closer to “lion” than it is to “linear algebra.” Unsupervised learning, through the Concept Matrix™, forms the basis of our understanding of semantic information  (remember our discussion above).

The Syntax Matrix™ helps us understand the most likely parsing of a sentence – forming the base of our understanding of syntax (again, recall our discussion earlier in this article).

Natural Language Processing broadly refers to the study and development of computer systems that can interpret speech and text as humans naturally speak and type it.

A phrase like “the bat flew through the air” can have multiple meanings depending on the definition of bat: winged mammal, wooden stick, or something else entirely?

Let’s return to the sentence, “Billy hit the ball over the house.” Taken separately, the three types of information would return: These aren’t very helpful by themselves.

This analysis can be accomplished in a number of ways, through machine learning models or by inputting rules for a computer to follow when analyzing text.

Over time, as natural language processing and machine learning techniques have evolved, an increasing number of companies offer products that rely exclusively on machine learning.

We’ve trained a range of supervised and unsupervised models that work in tandem with rules and patterns that we’ve been refining for over a decade.

In short: The best way to do machine learning for NLP is a hybrid approach: many types of machine learning working in tandem with pure NLP code.

For best practices in machine learning for NLP, read our companion article: Machine Learning Micromodels: More Data is Not Always Better To learn about the difference between tuning and training, and how to approach them, read our guide: Tune First, Then Train And, to learn more about general machine learning for NLP and text analytics, read our full white paper on the subject.

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