Natural Language Processing, or NLP, allows computers to understand the natural language of humans through artificial intelligence technology. Solutions that use natural language processing products deliver significant value to businesses that understand how to harness its potential.
The typical NLP implementation uses machine learning algorithms to process unstructured data and output it into a format that makes sense for computers. The two parts of language that NLP looks at in this data are the syntax and the semantics.
The syntax focuses on the grammatical rules of language and looks for the intent of the text based on this information. It’s also useful for identifying words that have similar meanings, segmenting phrases into words and units, finding the parts of speech in a sentence, looking at the root form of a particular word, and parsing what the sentence as a whole means.
Semantics looks into the meanings of words and phrases in context. You don’t always get the proper understanding of text based on purely grammatical analysis, especially when it comes to slang terms and phrases. When NLP tools look at semantics, it can interpret this data.
NLP Use Cases
Natural Language Processing NLP has many practical applications in the business world, no matter what your industry is. Here are some of the most common use cases you’ll find with this technology.
In an age of social media, your audience isn’t afraid to share their opinions of news. This type of direct feedback is market research gold, but only if you can process it to find trends, patterns, and overall impressions. Sentiment analysis accomplishes this by taking unstructured text data and analyzing it to determine key insights. Many companies have a wealth of data that they’re unable to access since sentiment analysis is not a process that you can manually do at scale. NLP automates a significant portion of the work so you can move forward with strategic decisions. It can create topics, run analytics on the text, and look for like words.
Chatbots are a popular way for companies to offer basic support and information 24/7 without hiring an exponentially growing amount of staff to do so. People who send the messages don’t need to use special keywords to interact with the bot. It picks up on the meaning of the person’s sentences to present them with relevant content. For more advanced requests, the bot is capable of connecting that individual with a staff member.
You end up quickly helping a large portion of your audience with their basic needs, as many requests fall into this category for the typical business. This part of your audience can be happy with the immediate assistance they received, even if it happens to be in the middle of the night. Your staff members get their time freed up so they can focus on providing an excellent customer experience for people with more complex requests. They don’t have to feel rushed off the phone due to a queue that grows by the second, and that allows you to cement a reputation for a customer-centric company without straining your resources as you grow.
Structuring Unstructured Data
Think about all of the unstructured data that ends up being untapped because you lack a way to work with it efficiently. NLP opens up the possibilities for this information, and some NLP solutions also support a mix of structured and unstructured data. As more data sources develop in the future, having a plan in place for the unstructured data is key to maintaining a competitive advantage.
Digital Personal Assistants
Digital personal assistants rely heavily on NLP to understand user requests, with a strong focus on voice input. Microsoft, Google, Apple, and Amazon all have their own versions of this use case, and popularity continues to grow for it. Whether you want to get your content on one of these platforms or you have a product that could benefit from voice control, there are many ways to implement this option in your organization.
Global companies may have text data in dozens or hundreds of different languages. The market needs in one country can be quite different from the next, especially when it comes to figuring out whether your marketing messages are appropriate for the region. Automating the translation process frees up staff time and increases their productivity when working with multi-language analysis. The automated translation feature may be included alongside other NLP tools or as a stand-alone solution.
Dictation is heavily used in many industries, especially in the medical field. Misinterpretation of voice data can lead to mistakes on medical records and potentially deadly consequences, so accurate transcription is an important issue. NLP works to improve speech-to-text tools by looking at the context of the discussion and what the person is trying to convey.
Voicemail transcription is another area where this use case shines. Rather than listening through each voicemail, you can get a basic overview of what someone is calling in about with NLP. This allows you to automatically assign messages to the right staff members, identify priority cases, and process the text for sentiment analysis and other insights.
Live captioning of live streaming content is another example of this use case. This type of tool automatically creates subtitles for live videos to make the content more accessible to people who are hard of hearing, have difficulties processing speech, or are deaf. It’s also useful for video viewers who have their sound muted.
Interactive Voice Response
Interactive voice responses on phone lines have improved significantly over the years through advances in NLP. These changes make it possible to better route calls, provide callers with the information that they need without staff intervention, identify whether someone is frustrated or irate and would benefit from talking to specialists in de-escalation and to track call metrics based on the reasons for calling.
Recruiting NLP Specialists
NLP is a highly flexible and valuable technology, and as such specialists in this area are in-demand. It can be challenging to recruit NLP specialists, especially if you want to assemble a data scientist team. There are a few options when you want to recruit in this competitive area.
- In-house: You would recruit, train, and hire the employee on as an in-house, full-time or part-time employee. In addition to the difficulties in finding these candidates, you also have to offer a significant benefits package that leads to many overhead costs with each hire.
- Outsourced: You avoid the overhead costs of an in-house hire and have the option to bring in an outsourced data science service based on your current projects. However, that service works with multiple clients and may not be able to scale up with you as you grow.
- Upskilling: This is the most cost-effective way to bring on a data scientist. Look for potential candidates in your workforce and pay for their training and upskilling. They’re already familiar with your organization, and investing in their skills in an area they’re interested in leads to a more engaged and loyal employee.
- Flexibility: Do you really need the data scientist to be in-house? If you have the opportunity to look at remote candidates, then you open up more possibilities for specialists. This option also allows you to make the position more accessible to disabled applicants, who may be unable to go to a traditional workplace, along with candidates who have responsibilities that require them to be close to home. For example, someone caring for an aging parent may not be able to be on-site.
Choosing an NLP Solution
NLP solutions come in all shapes and sizes. When you’re evaluating your options, consider the type of data that you’re working with, your business goals, the type of insights you hope to gain from unstructured data, and the resources that you have on-hand to implement the solution.
If you don’t have data scientists on-staff, for example, you probably don’t want to choose a barebone solution that requires custom development to use. All-in-one and comprehensive NLP solutions that are user-friendly can be excellent choices for non-technical business users and those that don’t need access to the machine learning engine.
NLP is an exceptionally useful technology for your organization and allows you to harness the power of all of your data. Think about the ways that you can make it work to support your business goals.