In 1950, Alan Turing proposed what he called the imitation game, now known as the Turing test, which was to be used to determine whether a machine can think. A computer passes this test if it can communicate with a human observer via text and the human concludes that the other party is also a human.
Ever since Turing’s day, cognitive scientists, artificial intelligence (AI) professionals, machine learning (ML) engineers, and linguists have sought to transfer the natural language ability of humans to machines, giving rise to the field of natural language processing (NLP).
NLP is about mimicking the ability of humans to process, analyze, interpret, or generate natural languages so that they can create models of the syntax and, importantly, comprehend their semantics.
NLP is a mix of technologies. It deals with analyzing and processing both speech data and textual content. Relevant areas include:
After more than 50 years of research, NLP technology has come into play in everyday applications used by both consumers and businesses. Use cases include:
Many organizations have vast collections of handwritten or printed books, documents, letters, archives, and records. Optical character recognition (OCR) software identifies and recognizes text from digitally scanned images of these documents for further processing, categorizing, indexing, and storage. OCR software not only uses techniques from computer vision but also uses language models to correctly interpret text from digital pictures.
Voice-based assistants are now available on all types of devices, ranging from desktop computers to mobile phones. This software uses techniques from NLP to communicate with a user via text or speech and carries out various commands.
Email platforms use NLP extensively for such tasks as spam filtering, auto-completion of words, phrase suggestions, sentence completion, and grammar checking.
Search engines rely heavily on algorithms from NLP to interpret user queries, retrieve information, rank and index pages, and more.
Machine translation deals with translating one human language to another. It requires building computational models of different languages to interpret one language and generate sentences in another.
Text analytics and text mining now make it possible to automatically parse text documents and categorize them for further indexing or archiving.
Speech readers use text-to-speech technology to convert textual data to regular speech. They are becoming a regular part of many apps that use speech as an interface to communicate with users, including assistive technologies for people who are blind or have low vision. Audio book apps are another example. They can read text aloud by employing text-to-speech technology.
And it can go the other way around: recognizing speech and converting it to text. Business use cases here include medical transcriptions and subtitle generation in news videos and other recordings.
AI and NLP technology aid document understanding. For example, companies carry out automatic resume parsing to filter job applications and determine a short list of candidates. You can also find automated tools that summarize lengthy documents using NLP. Summarizing multiple product reviews from multiple consumers is another application.
Many word processors and editors include tools for grammar checking and correction. NLP is used at the back end to generate language models that check whether input text is syntactically and grammatically correct.
Chatbots use AI and NLP technology to converse with users. They are used in various business sectors where they offer automated services to help customers place orders, make reservations, carry out banking services, book appointments, etc.
Many technologies can power recommender systems. NLP techniques are used to build user profiles from reviews, feedback, purchase history, site use, and comments. The system can recommend products and online content based on what it learns from each user’s profile.
NER technology deals with searching, locating, and identifying key entities within text and placing them in user-defined categories such as “people names,” “company names,” “geographic locations,” and so on. Once entities are identified, you can use NLP techniques to build knowledge graphs that relate one entity to another. Knowledge graphs create structured data from unstructured text and help interpret the semantics of text and its contextual meaning.
Imitating the natural language ability of humans is an ambitious and challenging task for several reasons.
One of the main challenges of NLP is the ambiguity present in natural languages. For example, one word may have different meanings in different contexts and different cultures. Similarly, ambiguities can arise at the sentence level. Interpreting the correct semantics of the language from context still presents a major hurdle for NLP.
The amount of data required to represent a complete language for developing multipurpose applications is huge. Along with high memory requirements in general, model building requires enormous computational resources for learning the diversity of words, phrases, and sentences.
Large amounts of data are required to train a domain-specific or general-purpose NLP application. Text or speech data is unstructured and so must be processed and labeled before being input to a learning algorithm. That process can be costly and time-consuming because of the requirement for large volumes of data.
Most NLP systems that deal with language understanding require domain knowledge as well as semantic models of the real world. Ontologies that show relationships between different objects and their properties have to be built. Representing and constructing ontologies is another challenge in NLP.
NLP is a powerful technology. It has already made its way into many domains including finance, marketing, health, HR, and more. But NLP is still being improved, including in these areas:
Many future NLP applications are likely to have a more interactive and humanlike interface for users. The front end might require natural speech or text as an interface and mode of input.
Conversational AI research is now more focused on creating advanced chatbots that can engage in question-and-answer sessions with humans using natural language.
Sentiment analysis, which attempts to interpret the emotions behind a textual statement about a product, for example (is a review positive, negative, or neutral?) has made tremendous progress in recent years. A focus of future research is embedding emotions into synthetic speech as well as into generated text.
In the future, you can look for more powerful deep learning models with improved accuracy in all kinds of tasks related to NLP.
Developing an NLP system with high accuracy requires computational resources. That’s why NLP services offered by various cloud platforms are becoming more popular and are likely to dominate in the future.
Now that you have a basic overview of NLP, take a deeper dive and see how your organization can benefit from it.
NLP offers an almost endless list of possibilities. From document understanding to text summarization, information mining, and intelligent communications, NLP can pave the way for future products and services.
Whether you develop your own system, use open-source software, or subscribe to an NLP cloud service, integrating this technology into your software can totally change the application’s look and feel and add more value for your users.