Natural Language Processing Lab
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Artificial intelligence and machine learning are having a major impact on countless functions across numerous industries. While these technologies are helping companies optimize efficiencies and glean new insights from their data, there is a new capability that many are just beginning to discover.
Natural language processing, or NLP for short, is a revolutionary new solution that is helping companies enhance their insights and get even more visibility into all facets of their customer-facing operations than ever before. In fact, a 2019 Statista report projects that the NLP market will increase to over $43 billion dollars by 2025. But what’s the big deal? Here is a breakdown of what exactly natural language processing is, how it’s leveraged, and real use case scenarios from some major industries.
What is Natural Language Processing?
Simply put, natural language processing utilizes AI and machine learning to extract meaning from text. In practice, that meaning is collected as part of a data set or to produce a desired outcome. A simple example of natural language processing would be:
Depending on the natural language programming, the presentation of that meaning could be through pure text, a text-to-speech reading, or within a graphical representation or chart.
Why Is NLP Difficult?
Understanding human language, with all of its intricacies, dialects, inflections and the like, can be difficult enough for people, let alone computers. The challenge up to this point has been how to get machines to understand not only the words, but also how the concepts are connected to deliver the intended message. How could a machine detect frustration in a response like “Oh, I’m having a great time” if it can’t recognize sarcasm? As a way of extracting meaning, NLP is revolutionary because, as TechTarget defines it,
“ “Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. The development of NLP applications is challenging because computers traditionally require humans to “speak” to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. Human speech, however, is not always precise — it is often ambiguous and the linguistic structure can depend on many complex variables, including slang, regional dialects and social context.” ”
The sheer number of variables that need to be accounted for in order for a natural learning process application to be effective is beyond the scope of even the most skilled programmers. This is where machine learning AIs have served as an essential piece of natural language processing techniques.
What’s the Difference Between NLP, AI, and ML?
AI is a general term for any machine that is programmed to mimic the way humans think. Where the earliest AIs could solve simple problems, thanks to modern programming techniques AIs are now able to emulate higher-level cognitive abilities – most notably learning from examples. This particular process of teaching a machine to automatically learn from and improve upon past experiences is achieved through a set of rules, or algorithms, called machine learning.
At its most basic, natural language processing is the means by which a machine understands and translates human language through text. NLP technology is only as effective as the complexity of its AI programming. The reason machine learning has been so essential to the advancement of natural language processing is that an NLP machine learning AI is able to continually improve itself to increase accuracy and correct misunderstandings at a rate a human programmer can’t possibly match.
Machine learning AIs have advanced to the level today where natural language processing can analyze, extract meaning from, and determine actionable insights from both syntax and semantics in text.
- Syntax analysis: NLP determines the meaning from a language based on grammatical rules of that language. NLP syntax techniques that are commonly used include parsing word segmentation, sentence breaking, morphological segmentation, and stemming.
- Semantics analysis: NLP can also determine meaning and context from language using algorithms to understand the meaning and structure of sentences. NLP semantics techniques include word sense disambiguation, named entity recognition, and natural language generation.
Benefits of NLP
Natural language processing is a cutting-edge development for a number of reasons. Before NLP, organizations that utilized AI and machine learning were just skimming the surface of their data insights. Now, NLP gives them the tools to not only gather enhanced data, but analyze the totality of the data — both linguistic and numerical data. NLP gets organizations data driven results, using language as opposed to just numbers.
Beyond just its awesome data analyzation capabilities, NLP has a number of benefits that a company in any industry would appreciate.
- NLP allows non-subject matter experts to obtain answers to their questions.
- It creates structure from a highly unstructured data source.
- It helps identify root causes of product issues quicker.
- NLP can identify profitable customers and understand the reasons for their loyalty.
- NLP can recognize and classify fraudulent behavior or claims.
- It can understand many different languages, jargon, and even slang.
- NLP can also reduce customer complaints by proactively identifying trends in customer communication.
- It can even understand competitors’ product offerings.
Natural Language Processing Capabilities
Because natural language processing is still in its infancy, its capabilities continue to expand on a seemingly weekly basis. Some of NLP’s most notable accomplishments and features include:
Smart assistants: Think of Siri and Alexa — these virtual smart assistants rely on natural language processing to understand inflection and tone to complete their tasks.
Search results: Search engines consistently utilize NLP to proactively understand searcher intent and provide relevant results faster. It can even generate responses based on similar search behaviors or trends.
Predictive text: This is one of the earliest examples of natural language processing in action. Things like autocorrect and auto complete are made possible by NLP, which can even learn personal language habits and make suggestions based on individual behavioral patterns.
Text analytics: Natural language processing can analyze text sources from email to social media posts and beyond to give companies insights beyond numbers and figures. NLP text analytics converts unstructured text and communication into actionable and organized data for analysis using different linguistic, statistical, and machine learning techniques.
Sentiment analysis: Sentiment analysis is arguably the most exciting feature of natural language processing. Early NLP capabilities allowed organizations to collect speech-to-text communication or other textural variants, but it couldn’t accurately determine its full meaning. Today, natural language processing can sort and understand the nuances and emotions in human voices and text, giving organizations unparalleled insight.
Text classification: Text classification allows NLP and AI technologies to automatically understand, process, and categorize unstructured text. Using natural language processing models, data could be sorted and organized based on predetermined tags and categories.
Text extraction: Also known as named entity recognition, natural language processing can automatically identify specific named entities within text, such as names and places, or more specialized information like keywords or even product names or descriptions.
Text summarization: Text summarization is most helpfully applied in academic, research, or healthcare settings, as it uses natural language processing to quickly process text and extract the most important information. NLP can summarize text based on exact key phrases within the text, or it can even summarize based on determined meanings and inferences, providing a paraphrased summary.
Market intelligence: Natural language processing can help marketers and market researchers by breaking down topics, sentiment, keywords, and intent in unstructured data from any variety of text or customer communication.
Intent classification: This capability allows companies to more accurately identify the purpose within text. It can benefit customer service teams and sales teams by more quickly identifying sales-ready leads through their emails, social media posts, and other communication.
Urgency detection: NLP algorithms can be established that look for key phrases or words that connotate urgency or stress in text. This can help companies prioritize their work or customer service outreach to those who have communicated in a such a manner.
Topic modeling: This NLP technique scans text documents for specific patterns in words or phrasing and automatically groups the text based on these trends. It can also identify consistent word groups and similar expressions that best characterize a set of documents and create a summary.
NLP Applications by Industry
Natural language processing is just beginning to demonstrate its true impact on business operations across many industries. Here are just some of the most common applications of NLP in some of the biggest industries around the world.
- Using sentiment analysis, financial institutions can analyze larger amounts of market research and data, ultimately leveraging that insight to make more informed investment decisions and streamline risk management.
- NLP can help banks and other security institutions identify money laundering or other fraudulent situations.
- Natural language processing can be leveraged to help insurers identify fraudulent claims. By analyzing customer communication and even social media profiles, AI can identify indicators of fraud and flag such claims for further inspection.
- Another way insurers can utilize natural language processing is in their monitoring of the ultra-competitive insurance market landscape. Using text mining and market intelligence features, insurers can get a better read of what their competitors are doing and plan what products to bring to market to keep up or get a step ahead of their competition.
Manufacturing & Supply Chain
- To improve automation efforts and streamline the manufacturing pipeline, NLP can analyze thousands of shipment documents and give manufacturers better insight into what areas of their supply chain are lagging. Using this info, they can make upgrades to certain steps in the process or make logistical changes to optimize efficiency.
- Manufacturers can leverage natural language processing capabilities by performing what is known as web scraping. NLP can “scrape” or scan online resources for information about industry benchmark rates for transportation rates, fuel prices, and labor costs. This data ultimately helps them compare their costs to market standards and identify cost saving opportunities.
- Retailers especially should be leveraging sentiment analysis. By analyzing customer sentiment toward their brands or products, retail companies can make more informed decisions across their business operations, from product rollout/discontinuation to sales and marketing initiatives and even individual store performance. NLP leverages social media comments, customers reviews, and more and turns them into actionable data that retailers can use to improve their weaknesses and ultimately strengthen the brand.
- NLP can also help improve customer loyalty by helping retailers understand it in the first place. By analyzing the communication, sentiment, and behavior of their most profitable customers, retail companies can get a better idea of what actions create more consistent shoppers. When they understand what keeps buyers coming back for more, they can proactively increase those actions.
- Natural language processing is just beginning to help the healthcare field, and its potential applications are numerous. Currently it is helping researchers battling the COVID-19 pandemic in a variety of ways, namely by analyzing incoming email and live chat data from patient help lines to flag those with potential COVID-19 symptoms. This has allowed physicians to proactively prioritize patients and get those in need of care into the hospital quicker.
- It can also help medical providers summarize, log, and utilize their notes and patient information faster and more accurately. Using text summarization and named entity recognition, typically lengthy medical information can be quickly summarized and categorized based on important related keywords. This can help improve diagnosis, treatments, and ultimately patient outcomes.
The first and most important ingredient required for natural language processing to be effective is data. Once businesses have effective data collection and organization protocols in place, they are just one step away from realizing the capabilities of NLP.
Get Started with Hitachi Solutions
The Hitachi Solutions team are experts in helping organizations put their data to work for them. Our accessible and effective natural language processing solutions can be tailored to any industry and any goal.
If you’re ready to make the most of your data and begin using NLP solutions, contact us — we’re ready to help you get started.
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