NLU, Human-Computer Interactions, New Software

NLU, Human-Computer Interactions, New Software

Natural Language Understanding or NLU is a sub-branch of Natural Language Processing or NLP that focuses on the enabling of human-computer interactions, whether it be in the form of speech or written language. In conjunction with Natural Language Generation or NLG, NLU enables the operation and functionality of a variety of services and programs that utilize NLP, including automatic transcription and translation software programs. More specifically, the main purpose of NLU is the development of voice and chat-enabled bots that can interact with the general public without the need for human intervention. As such, many multinational technology companies, such as Microsoft, Google, and Apple, are currently working on projects focused on the development and implementation of NLU.

How does Natural Language Understanding work?

NLU functions through the analysis of input texts, effectively using these texts to determine the meaning behind a specific user request. Otherwise known as NLU intent, intent in the context of NLU refers to a particular action that a user intends to perform. For example, when speaking to a popular voice assistant such as Amazon’s Alexa, a user may direct the AI to play the next song within their playlist. In order to effectively respond to this user command, Alexa will respond in accordance with the training data that was used to create the machine learning and artificial intelligence models that enable such programs to operate. To illustrate this point further, NLU analyzes data by using algorithms to transform human speech into a data model that contains both pragmatic and semantic definitions, also known as a structured ontology.

With this being said, the two fundamental concepts that enable this process are intent and entity recognition. As such, while intent focuses on specific user actions, entity recognition focuses on identifying the specific entities within a particular phrase or sentence. Furthermore, there are two different types of entities within entity recognition, named and numeric entities. Named entities are broken down into specific categories, such as locations, physical objects, and people, among others. Alternatively, as the name suggests, numeric entities are geared towards recognizing numbers, whether this is in the form of percentages, currencies, or measurements, just to name a few.

NLU training data

As all applications of machine learning and artificial intelligence function in accordance with sets of training data, NLU training data takes the form of sample utterances. Sample utterances are written examples of the kinds of topics or commands that human beings would be likely to express to a chat or voice bot. For instance, an individual might request a chatbot to provide them with a tracking number for an article of clothing that they ordered via an online website, or to make a reservation at a local restaurant. Through the combination of intent and entity recognition, computer programs are able to interpret human inputs. To provide an example, consider a request for a weekend getaway to Paris, France.

In such a scenario, plane tickets and hotel reservations would represent the intent, while Paris, France would represent the entity recognition. The combination of intent, entities, and sample utterances form the foundation of a language model within an NLP software program. In keeping with the example of Amazon’s Alexa, while Natural Language Generation allows such software to verbally communicate with humans, Natural Language Understanding allows the software to interpret the commands that humans provide them, effectively generating understanding in accordance with the training data or language model that was used to create the program. Moreover, when consumers interact with chatbots while browsing the internet, the implementation of NLU allows such chatbots to understand and respond in a timely manner.

While true language understanding is a concept that is unique to the human mind, as the surface level of words, phrases, and sentences is only a fraction of the meaning of human language and expression, Natural Language Understanding enables software programs to interact with human inputs on a basic level. This is perhaps best exhibited by the limitations of many popular AI assistants and chatbots, as these programs can handle specific commands extremely well, but quickly break when users make commands or requests that are outside the scope of the language models or training data that were used to create the program. As such, while NLP has already led to the development of numerous fascinating and innovative software programs, many more will be developed as artificial intelligence and machine learning algorithms continue to be improved upon.

Related Reads