With the advent and rise of chatbots, we are starting to see them utilize artificial intelligence — especially machine learning — to accomplish tasks, at scale, that cannot be matched by a team of interns or veterans. Even better, enterprises are now able to derive insights by analyzing conversations with cold math. However, there is still a substantial amount of people who carry a common sentiment every time they have a conversation with a bot – “It doesn’t understand what I’m saying.”
This is where Natural Language Processing comes into the picture. Within the right context for the right applications, NLP can pave the way for an easier-to-use interface to features and services. But more importantly, an NLP based chatbot can give the end users on the other side of the screen that they’re having a conversation, as opposed to going through a limited set of options and menus to reach their end goal.
What is Natural Language Processing (NLP)?
Natural Language Processing is based on deep learning that enables computers to acquire meaning from inputs given by users. In the context of bots, it assesses the intent of the input from the users and then creates responses based on a contextual analysis similar to a human being.
Say you have a chatbot for customer support, it is very likely that users will try to ask questions that go beyond the bot’s scope and throw it off. This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised.
When it comes to Natural Language Processing, developers can train the bot on multiple interactions and conversations it will go through as well as providing multiple examples of content it will come in contact with as that tends to give it a much wider basis with which it can further assess and interpret queries more effectively.
So, while training the bot sounds like a very tedious process, the results are very much worth it. Royal Bank of Scotland uses NLP in their chatbots to enhance customer experience through text analysis to interpret the trends from the customer feedback in multiple forms like surveys, call center discussions, complaints or emails. It helps them identify the root cause of the customer’s dissatisfaction and help them improve their services according to that.
What is the Best Approach towards NLP?
The best approach towards NLP is a blend of Machine Learning and Fundamental Meaning for maximizing the outcomes. Machine Learning only is at the core of many NLP platforms, however, the amalgamation of fundamental meaning and Machine Learning helps to make efficient NLP based chatbots. Machine Language is used to train the bots which leads it to continuous learning for natural language processing (NLP) and natural language generation (NLG). Both ML and FM has its own benefits and shortcomings as well. Best features of both approaches are ideal for resolving real-world business problems.
Here’s what an NLP based bot entails –
- Lesser false positive outcomes through accurate interpretation.
- Identify user input failures and resolve conflicts using statistical modeling.
- Use comprehensive communication for user responses.
- Learn faster to address the development gaps.
- Achieve natural language capability through lesser training data inputs.
- Ability to re-purpose the input training data for future
- Provide simple corrective actions for false positives.
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.
- Intent Recognition
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.
- Dealing with Entity
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.
- Capitalization of Nouns
NLP enabled chatbots to 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.
- Tense of the Verbs
AI chatbots understand different tense and conjugation of the verbs through the tenses.
- Contractions
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 Dialog flow (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 NLP 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.
2. 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 repetitive tasks every day. You can use NLP based chatbots for internal use as well especially for Human Resources and IT Helpdesk.
3. Reduced Cost
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.
4. 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.
5. 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.
Conclusion
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 aren’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 the 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 is a multitude of factors that you need to consider when it comes to making a decision between an AI and rule-based bot.
A version of this post originally appeared here.
via https://www.aiupnow.com
Mitul Makadia, Khareem Sudlow
