It is very common that we have many conversations with our intelligent personal assistants, which help you to grab the context at appropriate time and results are being presented to you in spoken language, which also provides you many links as well as the directions of the map.
These systems are based on Natural Language Processing (NLP) and help humans as well as machines for communicating in natural language. Natural Language Understanding (NLU) and Natural Language Generation (NLG) are subsets of NLP.
If we see an NLP system which is effective can easily comprehend the question and its meaning, and can easily determine the right action, and give the response in the same language which is easily understood by the user. It is stated by Alan Turing that if a person can easily get tricked by a machine then such a machine is artificially intelligent. And this came to be known as the Turning test and after passing it computer science goals are readily set. It is what NLP systems aim to achieve.
Apart from personal assistants like Siri, Alexa, and Google Assistant, some other applications of NLP include social media sentiment analysis, summarizing information and email spam filtering. NLP mainly includes speech recognition, speech synthesis, Natural Language Generation (NLG) and Natural Language Understanding (NLU). While speech recognition software transcribes spoken language, speech synthesis software focuses on text to speech conversion.
NLU attempts to understand the meaning behind the written text. After having the speech recognition software convert speech into text, NLU software comes into the picture to decipher its meaning. It is quite possible that the same text has various meanings, or different words have the same meaning, or that the meaning changes with the context. Knowing the rules and structure of the language, understanding the text without ambiguity are some of the challenges faced by NLU systems. Popular applications include sentiment detection and profanity filtering among others. Google acquired API.ai provides tools for speech recognition and NLU.
NLG does exactly the opposite. Given the data, it analyzes it and generates narratives in conversational language. It goes way beyond template-based systems, having been configured with the domain knowledge and experience of a human expert to produce well-researched, accurate output within seconds. Narratives can be generated for people across all hierarchical levels in an organization, in multiple languages.
NLP vs NLU vs NLG:
Although they may come across as intimidating technical jargons NLP, NLG, and NLU are seemingly complex acronyms used to explain straightforward processes. Here the breakdown:
- NLP is when computers read and turn input text into structured data
- NLU means an understanding of the textual and statistical data captured by computers
- NLG is when computers turn structured data into text and write information in human language
The reading part of Natural Language Processing is complicated and includes many functions such as:
- Language filters for indecent expressions
- Sentiment analysis for human emotions involved
- Subject matter classification
- Location detection
Natural Language Understanding is an important subset of Artificial Intelligence and comes after Natural Language Processing to genuinely understand what the text proposes and extracts the meaning hidden in it. Conversational AI bots like Alexa, Siri, Google Assistant incorporate NLU and NLG to achieve the purpose.
Understanding the potential of NLG
For formulating the new data and then communicating them, humans have always needed the data. However, enterprises need to identify the ways to streamline with a major influx of data that needs to be assessed along with the need to reduce costs significantly.
Coming to Natural Language Generation, the primary advantage lies in its ability to convert the dataset into legible narratives understood by humans. Upon processing statistical data present in spreadsheets, NLG can produce data-rich information, unlike Natural Language Processing that only assesses texts to form insights.
With Natural Language Generation, you can easily access the data, and analyze it and can communicate it with precision, scale and accuracy. With smart automation, activities having a high return can be more focused by humans and productivity surges.
In an interesting use case, The Associated Press leveraged the report-generating capability of Natural Language Generation to develop reports from corporate earnings data. This means they no longer need human reporters dedicating their time and energy wading through pools of data and then writing a report. Instead, as NLG produces thousands of narratives automatically once perfectly set up, they can invest their resources in performing more critical tasks.