Natural Language Processing

When Joaquin Phoenix fell in love with ‘Samantha’, in the 2013 film ‘Her’, he sets about creating a meaningful relationship with an operating system that is artificially intelligent, and able to communicate with him in a language he can understand.

At the time the film was released, Apple’s Siri technology had been in the market, and in the hands of users for about two years, so the concept of speaking to a ‘smart’ device, and having it speak back to you wasn’t something entirely foreign to audiences. In the world Spike Jonze created in ‘Her’, this technology had evolved far enough that a human was able to develop a real emotional connection to it.

In reality, we’re not quite at the point where an exchange with your computer or smart device may lead you to romantic feelings, but it does make us consider where the technology is headed.

What is Natural Language Processing?

The technology that drives Siri, Alexa, the Google Assistant, Cortana, or any other ‘virtual assistant’ you might be used to speaking to, is powered by artificial intelligence and natural language processing. It’s the natural language processing (NLP) that has allowed humans to turn communication with computers on its head. For decades, we’ve needed to communicate with computers in their own language, but thanks to advances in artificial intelligence (AI) and NLP technology, we’ve taught computers to understand us.

In a technical sense, NLP is a form of artificial intelligence that helps machines “read” text by simulating the human ability to understand language.?NLP techniques incorporate a variety of methods to enable a machine to understand what’s being said or written in human communication—not just words individually—in a comprehensive way. This includes linguistics, semantics, statistics and machine learning to extract the meaning and decipher ambiguities in language.

How is it used?

Frequently used in online customer service and technical support, chatbots help customers speak to ‘someone’ without the wait on the telephone, answering their questions and directing them to relevant resources and products, 24 hours a day, seven days a week.

In order to be effective, chatbots must be fast, smart and easy to use, especially in the realm of customer service, where the user's expectation is high, and if they’re experiencing a technical issue, their patience may be low. To accomplish the expected level of service,?chatbots are created using NLP?to allow them to understand language, usually over text or voice-recognition interactions,?where users communicate in their own words, as if they were speaking (or typing) to a real human being. Integration with semantic and other cognitive technologies that enable a deeper understanding of human language allow chatbots to get even better at understanding and replying to more complex and longer-form requests.

In a research context, we’re now seeing NLP technology being used in the application of automated transcription services (link out NVivo transcription). Transcription is one of the most time-intensive tasks for qualitative, and mixed methods researchers, with many transcribing their interviews and focus group recordings themselves by hand. Unless you’re an incredibly fast and accurate typist, this is an incredibly laborious task, taking researcher’s time away from the actual analysis of their data.

Automated transcription tools utilize NLP technology to ‘listen’ to recordings of data such as focus groups, and interviews, and interpret them and produce them into a format and language that is useful for the researcher to go on and analyze, either manually, or using software.

Future uses of NLP

The NLP market size is estimated to grow to USD 16.07 Billion by 2021, globally, giving us a strong indication that NLP technology has huge growth opportunities across a number of sectors.

An understanding of human language can be especially powerful when applied to extract information and reveal meaning or sentiment in large amounts of text-based content?(or unstructured information), especially the types of content that has typically been manually examined by people.

Analysis that accurately understands the subtleties of language, for example, the choice of words, or the tone used, can provide useful knowledge and insight. NLP will play an important part in the continued development of tools that assist with the classification and analysis of data, with accuracy only improving as technology evolves.

Academics at the University of Bologna have applied NLP to the most used part of any academic article: the bibliography. A group of researchers are developing tools that can extract information on citations using natural language processing and common ontologies (representations of concepts and their relationships) that can be openly accessed and connected to other sources of information. The idea of the project is to enrich the bibliography in order to give the reader more comprehensive information about each single entry, instead of looking at the bibliography as one large piece of information.

In the commercial word, NLP analysis will have uses especially in the analysis of the typically carefully worded language of annual reports, call transcripts and other investor-sensitive communications, as well as legal and compliance documents. Effective analysis of sentiment in customer interactions will allow for organizations to make improvements in their product and service delivery outcomes.

NLP will be essential to the future of research

More effective and accurate understanding between humans and machines will only strengthen the efficiencies and outputs of those who need to understand and analyze unstructured data.

No matter where it is applied, NLP will be essential in understanding the true voice of the research participant, the customer, or the user and facilitating more seamless interaction and interpretation?on any platform where language and human communication are used.

To read more about automation, AI technology, and its effect on the research landscape, download this free whitepaper Transparency in an Age of Mass Digitization and Algorithmic Analysis.