Natural Language P
What is natural language processing?
Natural language processing is a branch of artificial intelligence that aims to help computers to understand human language input in the form of text or speech.
NLP combines multiple disciplines, including computation linguistics, machine learning, deep learning, and statistics.
These technologies work together to essentially give computer software the ability to process and understand human language in the way that another human could, including its meaning, intent, and sentiment.
NLP technology is used in a variety of applications including:
- Digital assistants such as Siri
- Speech-to-text dictation software
- Voice-operated GPS systems
- Customer service chatbots
- Predictive text
- Digital voicemail
- Autocorrect
- Search autocomplete
- Email filters
Additionally, companies are increasingly using NLP to create enterprise solutions that help businesses simplify processes, increase productivity, and streamline operations.
The benefits of employing natural language processing
It’s standard these days for companies to collect, store, process, and analyze large quantities of numerical data in order to generate valuable insights that can improve results.
Natural language processing opens up and empowers businesses to make smarter decisions that are based on larger sets of data. Further, this collection and analysis process happens quickly, especially compared to traditional methods.
For this reason, natural language processing has a number of relevant advantages.
When working with so much data, you’ll be able to generate insights to improve customer experience with the launch of new products.
On top of that, using NLP helps businesses become more efficient by automating work processes that require reviewing or analyzing texts. This frees up employees to work on other needle-moving tasks.
Taken together, you’re bound to see improved productivity, reduced costs, and an uplift in revenue.
Top Used NLP Techniques
NLP is a rich field requiring the use of a number of different techniques in order to successfully process and understand human language. Below, we review and define a selection of the techniques commonly used in NLP technology.
Tokenization
Also called word segmentation, tokenization is one of the simplest and most important techniques involved in NLP.
It’s a crucial preprocessing step in which a long string of text is broken down into smaller units called tokens. Tokens include words, characters, and sub words. They are the building blocks of natural language processing, and most NLP models process raw text on the token level.
Stemming & lemmatization
After tokenization, the next preprocessing step is either stemming or lemmatization. These techniques generate the root word from the different existing variations of a word.
For example, the root word “stick” can be written in many different variations, like:
- Stick
- Stuck
- Sticker
- Sticking
- Sticks
- Unstick
Stemming and lemmatization are two different ways to try to identify a root word. Stemming works by removing the end of a word. This NLP technique may or may not work depending on the word. For example, it would work on “sticks,” but not “unstick” or “stuck.”
Lemmatization is a more sophisticated technique that uses morphological analysis to find the base form of a word, also called a lemma.
The difference between how stemming and lemmatization work is illustrated in this image from itnext, using different forms of the word “change.”
Morphological segmentation
Morphological segmentation is the process of splitting words into the morphemes that make them up. A morpheme is the smallest unit of language that carries meaning. Some words such as “table” and “lamp” only contain one morpheme.
But other words can contain multiple morphemes. For example, the word “sunrise” contains two morphemes: sun and rise. Like stemming and lemmatization, morphological segmentation can help preprocess input text.
Stop words removal
Stop words removal is another preprocessing step of NLP that removes filler words to allow the AI to focus on words that hold meaning. This includes conjunctions such as “and” and “because,” as well as prepositions such as “under” and “in.”
By removing these unhelpful words, NLP systems are left with less data to process, allowing them to work more efficiently. It isn’t a necessary step of every NLP use case, but it can help with things such as text classification.
Text classification
Text classification is an umbrella term for any technique used to organize large quantities of raw text data. Sentiment analysis, topic modeling, and keyword extraction are all different types of text classification. And we’ll talk about them shortly.
Text classification essentially takes unstructured text data and structures it, preparing it for further analysis. It can be used on nearly every text type and help with a number of different organization and categorization applications.
In this way, text classification is an essential part of natural language processing, used to help with everything from detecting spam to monitoring brand sentiment.
Some possible applications of text classification include:
- Grouping product reviews into categories based on sentiment.
- Flagging customer emails as more or less urgent.
- Organizing content by topic.
Sentiment analysis
Sentiment analysis, also known as emotion AI or opinion mining, is the process of analyzing text to determine whether it is generally positive, negative, or neutral.
As one of the most important NLP techniques for text classification, sentiment analysis is commonly used for applications such as analyzing user-generated content. It can be used on a variety of text types, including reviews, comments, tweets, and articles.
The Revuze platform employs sentiment analysis to understand how customers feel about various aspects of products. This allows companies to gain insights about consumers’ needs in real-time, and act accordingly to improve overall CX.