The machines working with NLP can understand sentence structure, idioms, and has machine-learned pattern recognition to try to match what you tell them and the action related. The machine is programmed to identify what people want from it and act upon them.
Natural Language Processing (NLP) is formally the “ability of machines to understand and interpret human language the way it is written or spoken”. The objective of NLP is to make computer/machines as intelligent as human beings in understanding language. The ultimate goal of NLP is to the fill the gap how the humans communicate and what the computer understands.
Linguistic analysis before performing NLP:
- Syntax – What part of given text is grammatically true.
- Semantics – What is the meaning of given text?
- Pragmatics – What is the purpose of the text?
In order to let the machine perform NLP is necessary that it can understand also:
- Grammatically correct parts in the text
- Meaning of the text
- Purpose of the text
Why would we need NLP?
With NLP, it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time. Due to the presence of a large amount of data (text) around, automatizations like chatbots can be an easy way to solve many tasks at the same time.
NLP is a big win in the discovery phase of products. The bot can help you to funnel the product range, driving the webpage user to a call to purchase.
NLP is suitable for conversational e-commerce. For instance every online booking or an event organization. It makes possible to find patterns in the unstructured data. It can solve big problems by elaborating Big Data in any business area like:
- Financial Institutions
The benefits of natural language processing are innumerable. Natural language processing can be leveraged by companies to improve the efficiency of documentation processes, improve the accuracy of that documentation, and identify the most pertinent information from large databases.
How a chatbot works?
Chatbots learn each time they make interaction with the user trying to match the user queries with the information in the knowledge base using machine learning.
If a user can just ask for what he needs, it will give a much better user experience than navigating through menus or trying to find the right combination of keywords for your search box.
Chatbots learn each time they make interaction with the user trying to match the user queries with the information in the knowledge base using machine learning and natural language processing.
The NLP tools used for making conversational bots are made mostly on Natural Language Understanding or NLU.
NLU is the study of understanding natural language. It will analyze a sentence and try to understand what the user meant. That way a user can just ask for everything he needs, instead of finding and clicking plenty of buttons.
NLU tries to understand the meaning of given text. The nature and structure of each word inside text must be understood in order to have NLU. For understanding the structures, NLU tries to resolve the following ambiguities present in natural language:
- Lexical Ambiguity – Words have multiple meanings
- Syntactic Ambiguity – Sentence having multiple parse trees
- Semantic Ambiguity – Sentence having multiple meanings
- Anaphoric Ambiguity – Phrase or word which is previously mentioned but has a different meaning
NLU is the part of NLP that tries to understand the structure and meaning of the text. The NLU checks if:
- Words have multiple meanings
- Sentences have multiple parse trees
- Sentences have multiple meanings
- A phrase or a word which is previously mentioned has a different meaning
Future uses of chatbots and NLP
Big Data comes from information stored in big organizations as well as enterprises. Examples include information of employees, company purchase, sale records, business transactions, the previous record of organizations, social media etc. As written above, one of the main function of chatbots is helping businesses with their data. Considering that today around 80 % of total data is available in the raw form, chatbots can easily store and analyze big amount of BD. Though humans use language, which is ambiguous and unstructured to be interpreted by computers, with the help of NLP, this huge unstructured data can be harnessed for evolving patterns inside data to better know the information contained in it.
Concluding, any business like retail, healthcare, business, financial institutions can benefit of chatbots and its technology since it would be the future of the customer service and also the best way to manage large amount of customers and data.