Artificial intelligence has revolutionized the world of technology in many ways over the last few years, with natural language processing (NLP) being one of the most critical subfields. NLP has been used by many firms to write an article, including The Guardian.
While NLP is not a new science, there are plenty of things about this AI tech that aren’t widely known. We’ve created a guide that gives you all the information you need to know about NLP.
What is Natural Language Processing?
Natural Language Processing, or NLP for short, refers to a branch of artificial intelligence that allows machines to break down and interpret text and spoken words like humans do. NLP research at Google uses a combination of computational linguistics with statistical, machine learning, and deep learning models to “understand” the whole meaning of a text or voice data, including the speaker or writer’s intent and sentiment.
Use Cases of Natural Language Processing
NLP drives many computer programs, particularly those designed to translate text from one language to another or summarize large volumes of text in a snap. There is a good chance you have already interacted with an NLP-driven program. Some examples of programs with NLP are voice-operated GPS systems, digital assistants, grammar correction systems, speech-to-text dictation software, and customer service chatbots.
NLP is also used in enterprise solutions designed to streamline business operations or increase employee productivity.
There are many other use cases of NLP. Here are some examples:
- NLP allows medical professionals to recognize and predict diseases based on a patient’s electronic health records and speech. For instance, Amazon Comprehend Medical uses NLP APIs (application programming interfaces) to extract disease conditions and treatment outcomes from patient notes and other electronic health records.
- NLP allows a company to determine what existing customers say about their products or services by extracting information through online channels, such as social media platforms. For example, sentiment analysis helps a business monitor whether there are positive, negative, or neutral feelings toward its products
- Google NLP is used to analyze your emails and categorize whether a message contains spammy content
- Some institutions have developed a system that uses NLP to identify whether a news source is accurate or politically biased
- Amazon and Apple have used NLP to develop voice-driven interfaces to respond to vocal prompts and perform search queries
- Financial traders use NLP to track news articles and comments about any possible mergers between companies
NLP is particularly booming in the healthcare industry, with the technology being used to improve medical services, including disease diagnosis, improving care experience, and improving clinical documentation.
Apart from improving the medical care experience, some companies are making huge advancements in medical treatments. Winterlight Labs, for example, is using NLP API to monitor cognitive impairments in people suffering from Alzheimer’s disease. Stanford University also used NLP to develop a chatbot therapist to help people suffering from anxiety.
Natural Language Processing (NLP) Techniques
There are six main NLP techniques that are used to extract data from text.
1. Sentiment Analysis
Sentiment analysis is an analysis technique used to determine whether a statement is positive, neutral, or negative. Sentiment score ranges between -1.0 (negative sentiment) and 1.0 (positive sentiment).
Sentiment analysis is used to transform large numbers of customer feedback into actionable, quantified results. The data you get from sentiment analysis can be analyzed further to give you customer insight and strategic results.
2. Named Entity Recognition (NER)
Named Entity Recognition is an NLP technique that tags “named identities” within statements and later extracts them for further entity analysis. NLP focuses on understanding and processing these entities to derive meaning from the text. By recognizing and categorizing entities, NLP systems can comprehend the context and relationships within the text.
NER works similarly to sentiment analysis. However, it only tags the identities of words and keeps track of how many times a certain type of entity appears within a certain text data.
For example, CreateVenue will be tagged as an organization and Canada will be tagged as a location.
The number of times an identity pops up in customer feedback can point to a need for you to fix a customer pain point, regardless of whether there are inefficiencies in product experience or customer experience.
Having customer data on hand can help you custom-tailor a journey to fit an individual user, which can significantly improve customer experience.
3. Text Summary
Text summarization is a technique that uses NLP to break down jargon into the simplest terms. For example, the World Health Organization’s statements on COVID-19 can be run through a text summarizer to get a concise output that is easily understandable by the public.
4. Topic Modeling
Topic modeling utilizes artificial intelligence programs to tag and group text clusters sharing common topics.
5. Text Classification
Text classification is a technique that utilizes NLP to organize a large amount of raw text data and structure it for further analysis. The technique is often used in mining data from customer reviews and service slogs.
6. Keyword Extraction
Keyword extraction is an automated process wherein NLP and machine learning algorithms are used to extract relevant information from text. This technique is often utilized to search for keywords relevant to your niche.
Entities in NLP:
Entities are objects or concepts that have a distinct and independent existence. In NLP, entities can be anything from people and organizations to locations, dates, and more.
Named Entity Recognition (NER) is a common NLP task that involves identifying and classifying entities in text. This helps in extracting information such as names of people, organizations, locations, dates, and other specific terms.
Connection:
NLP focuses on understanding and processing these entities to derive meaning from the text. By recognizing and categorizing entities, NLP systems can comprehend the context and relationships within the text.
Where Can You Get Natural Language API?
There are many NLP APIs on the market. However, our recommendation is to use Google. This is because the models are trained off of their massive data set of actual user searches. Moreover, Google has developed Google Cloud, also known as GCloud, which offers a full suite of NLP products and solutions.
The Google Natural Language API (Google NLP API) provides pre-trained models allowing developers to work with natural language features, including sentiment and entity analysis. In fact, the algorithm that provides the result is the same algorithm that determines your page rank on the SERPs: so these models have been pre-trained based off of actual users’ search queries, and the use in SEO is highly beneficial to marketers eg. When doing KW or topical research.
In addition, Google Cloud also offers Automated Machine Learning (AutoML) products that allow users to build and deploy custom machine learning models and natural language API even without any expertise in machine learning.
The suite offers a number of products, including:
Google Cloud Natural Language API, which allows users to derive insights from unstructured texts;
AutoML Natural Language, which lets users analyze and categorize documents as well as identify entities;
Dialogflow, which is used to build conversational interfaces for websites and mobile apps, and
Document AI, which is designed to read and understand documents so users can extract value from the data mine.
Benefits Of Using Natural Language API
Now that we’ve covered the basics of NLP, it’s time for you to learn how your business could benefit from utilizing this tool. Here is a list of benefits you can expect from utilizing natural language processing in AI.
Better data analysis
Unstructured data, such as documents and research studies, can be difficult for computers to process. However, NLP technology can easily analyze large amounts of text-based information with accuracy.
For example, NLP can be used to help HR teams review hundreds of resumes and immediately screen them for desired characteristics.
Better customer experience
Many businesses, especially those in the hospitality sector, depend on surveys and reviews to better understand their customer’s behavior. NLP can be utilized to recognize sentiment in customer messages and identify elements that may suggest an underlying emotion in the structure of a sentence.
For other businesses, NLP technology can be used to shorten response times to questions through chatbots.
More engaged employees
Using NLP can help you remove repetitive functions from your employees’ day-to-day tasks. This not only frees them to work on other higher-level projects, but also helps your company create a more productive workforce.
With NLP, your employees can process big data from multiple sources in real-time and expect a more comprehensive data set. Employees can then use the information to respond to customer requests or complete tasks.
Reduce costs
By empowering your employees and streamlining your process, you can create a more efficient operation that is better for your bottom line. For example, instead of needing ten people to respond to your customer’s queries, an NLP solution can reduce the number to two. This allows your eight other employees to focus on other tasks.
In Closing
If you’ve been following the latest trends in AI, you’d know that NLP is used in an ever-growing list of applications. Our blog post discussed various NLP techniques and tasks that you can utilize for your company.
If you want to find out more about how your company could benefit from NLP, or if you’re looking for a team of SEO experts who have experience in developing NLP solutions for your business, connect with us today.
Frequently Asked Questions
Which search engines use natural language processing?
Both Google and Bing use NLP to make their search function more relevant to conversational language and make it easier for users to find answers to their queries.
What is the salience score?
The salience score of a certain entity provides information about the importance of that entity to the entire document. Salience scores closer to 1.0 are highly salient, while scores closer to 0 are less salient.
Can NLP be used for semantic analysis?
Semantic analysis is actually a subfield of Natural Language Processing and Machine Learning. Semantic analysis helps in understanding the context of a text and identifying the emotions that might be in a certain sentence.
