Have you ever put any thought into how simple it is for humans to understand language? Just think: in everyday conversation, we convey our thoughts through a series of sounds that make up a language. How our brains are capable of translating so much unstructured data into useful information can be unfathomable.
Language does not come as naturally for machines; it takes a considerable amount of programming for them to understand even basic statements. So how do they do it? With NLP.
Natural Language Processing (NLP) is a concept that is still very abstract to most--even those that are in the IT industry can struggle with the idea. NLP is a type of “program” designed for computers to read, analyze, understand, and derive meaning from natural human languages in a way that is useful. It is used to analyze strings of text to decipher its meaning and intent.
In a nutshell, NLP is a way to help machines understand human language.
It is far more complex than that, however; think about how many ways a single sentence can be spoken and understood by the recipient. Here is a brief example of things that all are asking the same thing, but can be spoken completely different:
Natural Language Processing will take the intention of the request and find the most appropriate answer to your question in the same way another human would.
By utilizing natural language processing, developers are able to organize and structure knowledge to perform various tasks. NLP is used in a plethora of technologies, many of them now being commonplace. Some of the types of NLP that are used every day:
NLP is a series of complex algorithms that produce annotations over pieces of text. These pieces of text are identified as separate parts of speech, including dependency relations between words and the meaning behind each word if there are multiple entries in a dictionary for the same word (for example: minute [60 minutes in an hour] and minute [his problem was minute], et cetera). Because of this variance in language, particularly the English language, it is not a complete guarantee that a machine is able to understand a sentence's meaning perfectly every time.
Once all of the text has been identified, the machine will use queries to extract the meaning behind each sentence and gather the important data from them.
A well-known tale of NLP is dated to the 1950s, where Americans were trying to use machines to translate between the Russian and English languages. When they entered the biblical sentence, “The spirit is willing, but the flesh is weak” and translated it to Russian and then back to English, they had a result that was not intended:
The vodka is good, but the meat is rotten.”
It isn’t widely accepted as fact so much as it is a humorous story.
Approximately 80 percent of all data that is on the Internet is considered unstructured. This data ranges from chat messages, social media, images being shared, e-mails, and more. It is difficult for an individual to analyze all of this data and compile it into useful information, but for a well-trained machine it is just another day’s work. Computers utilize natural language processing to identify important strings of data.
A recent trend for businesses has been the surge of chatbots, designed to reduce HR costs by answering a user’s questions with the ability to pull up most requested information (for example, “how many days off do I have available?”). If the chatbot doesn’t understand or doesn’t have access to the data requested, they are able to automatically create a ticket and escalate to someone who can help them.
NLP can also use language patterns to understand the emotion of written messages, such as an upset customer’s tweet or a satisfied client’s Facebook status, and can tag it as positive, negative, or neutral. The benefit behind this is that you can quickly find and reach out to unhappy customers to try to improve their experience and win them over, turn satisfied customers into referral sources, and more.
Does your company utilize natural language processing for any of its functions? Are you looking into using it? Share your story in the comments below!