What can NLP Engines do?
NLP engines extensively use Machine Learning to parse user input in order to take out the necessary entities and understand user intent. Chatbots with Natural Language Processing can parse multiple user intents to minimize the failures.
User inputs through a chatbot are broken and compiled into a user intent through few words. For e.g., “search for a pizza corner in Seattle which offers deep dish margherita”.
NLP analyses complete sentence through the understanding of the meaning of the words, positioning, conjugation, plurality, and many other factors that human speech can have. Thus, it breaks down the complete sentence or a paragraph to a simpler one like — search for pizza to begin with followed by other search factors from the speech to better understand the intent of the user.
Entities can be fields, data or words related to date, time, place, location, description, a synonym of a word, a person, an item, a number or anything that specifies an object. The chatbots are able to identify words from users, matches the available entities or collects additional entities of needed to complete a task.
NLP enabled chatbots remove capitalization from the common nouns and recognize the proper nouns from speech/user input.
- Expansion & Transfer of Vocabulary
NLP enables bots to continuously add new synonyms and uses Machine Learning to expand chatbot vocabulary while also transfer vocabulary from one bot to the next.
AI chatbots understand different tense and conjugation of the verbs through the tenses.
Bots with NLP can expand the contractions and simplify the tasks removing apostrophes in between the words.
Other than these, there are many capabilities that NLP enabled bots possesses, such as — document analysis, machine translations, distinguish contents and more.
NLP engines rely on the following elements in order to process queries –
- Intent — The central concept of constructing a conversational user interface and it is identified as the task a user wants to achieve or the problem statement a user is looking to solve.
- Utterance — The various different instances of sentences that a user may give as input to the chatbot as when they are referring to an intent.
- Entity — They include all characteristics and details pertinent to the user’s intent. This can range from location, date, time, etc.
- Context — This helps in saving and share different parameters over the entirety of the user’s session.
- Session — This essentially covers the start and end points of a user’s conversation.
There are many NLP engines available in the market right from Google’s Dialogflow (previously known as API.ai), Wit.ai, Watson Conversation Service, Lex and more. Some services provide an all in one solution while some focus on resolving one single issue.
At its core, the crux of natural language processing lies in understanding input and translating it into language that can be understood between computers. To extract intents, parameters and the main context from utterances and transform it into a piece of structured data while also calling APIs is the job of NLP engines.
Why does your chatbot need Natural Language Processing?
There are many different types of chatbots created for various purposes like FAQ, customer service, virtual assistance and much more. Chatbots without NLP rely majorly on pre-fed static information & are naturally less equipped to handle human languages that have variations in emotions, intent, and sentiments to express each specific query.
Let’s check out the 5 reasons that your chatbot should have Natural Language Processing in it –
- Natural Conversations across Languages
The problem with the approach of pre-fed static content is that languages have an infinite number of variations in expressing a specific statement. There are uncountable ways a user can produce a statement to express an emotion. Researchers have worked long and hard to make the systems interpret the language of a human being.
Through NLP, it is possible to make a connection between the incoming text from a human being and the system generated response. This response can be anything starting from a simple answer to a query, action based on customer request or store any information from the customer to the system database. NLP can differentiate between the different types of requests generated by a human being and thereby enhance customer experience substantially.
- NLP based chatbots are smart to understand the language semantics, text structures, and speech phrases. Therefore, it empowers you to analyze a vast amount of unstructured data and make sense.
- NLP is capable of understanding the morphemes across languages which makes a bot more capable of understanding different nuances.
- NLP gives chatbots the ability to understand and interpret slangs and learn abbreviation continuously like a human being while also understanding various emotions through sentiment analysis.
- Focus on Mission Critical Tasks
Generally many different roles & resources are deployed in order to make an organization function, however, that entails repetition of manual tasks across different verticals like customer service, human resources, catalog management or invoice processing. NLP based chatbots reduce the human efforts in operations like customer service or invoice processing dramatically so that these operations require fewer resources with increased employee efficiency.
Now, employees can focus on mission critical tasks and tasks that impact the business positively in a far more creative manner as opposed to losing time on tedious repeated tasks every day. You can use NLP based chatbots for internal use as well especially for Human Resources and IT Helpdesk.
Costing is an essential aspect for any business to grow and increase profitability. NLP based chatbots can significantly assist in cutting down costs associated with manpower and other resources entangled in repetitive tasks as well as costs on customer retention, while improving efficiency and streamlining workflows.
- Higher Customer Satisfaction
Millennials today want an instant response and instant solutions for their queries. NLP helps chatbots understand, analyze and prioritize the questions according to the complexity & this enables bots to respond to customer queries faster than a human being. Faster responses help in building customer trust and subsequently, more business.
You’ll experience an increased customer retention rate after using chatbots. It reduces the effort and cost of acquiring a new customer each time by increasing loyalty of the existing ones. Chatbots give the customers the time and attention they want to make them feel important and happy.
- Market Research and Analysis
You can get or generate a considerable amount of versatile and unstructured content just from social media. NLP helps in structuring the unstructured content and draw meaning from it. You can easily understand the meaning or idea behind the customer reviews, inputs, comments or queries. You can get a glimpse at how the user is feeling about your services or your brand.
NLP based chatbots can help enhance your business processes and elevate customer experience to the next level while also increasing overall growth and profitability. It provides technological advantages to stay competitive in the market-saving time, effort and costs that further leads to increased customer satisfaction and increased engagements in your business.
Although NLP, NLU and NLG isn’t exactly at par with human language comprehension, given its subtleties and contextual reliance; an intelligent chatbot can imitate that level of understanding and analysis fairly well.
Within semi restricted contexts, a bot can execute quite well when it comes to assessing the user’s objective & accomplish required tasks in the form of a self-service interaction.
While NLP alone is the key and can’t work miracles or make certain that a chatbot responds to every message effectively, it is crucial to a chatbot’s successful user experience. There are a multitude of factors that you need to consider when it comes to making a decision between an AI and rule-based bot. At Maruti Techlabs, we build both types of chatbots, for a myriad of industries across different use cases, at scale.
If you’d like to learn more or have any questions, drop us a note on firstname.lastname@example.org — we’d love to chat.