Chatbots have become increasingly popular in recent years due to their ability to provide efficient and personalized customer service and assist with various tasks. With advancements in natural language processing (NLP) technology, chatbots can now engage in human-like conversations and understand the intent behind user input.
The reality is that modern chatbots utilizing NLP are identical to humans. Thus, it is no longer science fiction.
And that's because chatbot software incorporates natural language processing.
NLP lays the foundation for a simple user interface, features, and services with the proper context and tool. And chatbots that use NLP will drastically change how we communicate with people. It's already moving in the direction of casual talk.
Learning human speech and understanding how such technology works has never been more important. The natural language processing (NLP) market will reach USD 44.96 billion by 2028, predicts Reports and Data analysis.
Our daily lives and our businesses can both benefit immensely from natural language processing. You will get an in-depth explanation of launching your NLP chatbot in this blog.
What are Chatbots?
A chatbot is a clever program that automates tasks for humans and aids businesses in responding to common client questions. Most companies today utilize chatbots in various ways to respond to customers as quickly as possible across all industries. The main motive for businesses to adopt chatbots is to increase website traffic.
A chatbot prompts customers to enter their name, email address, and inquiry. If a problem is minor, such as a product defect, a booking error, or a primary information request, it can be resolved automatically; but, if the issue is severe, it will transfer the details to the human head and make it easier for the client to contact the organization manager. Additionally, the majority of customers prefer to communicate with chatbots.
What is NLP?
Natural language processing (NLP), which enables computers to comprehend user data, is built on profound learning principles. It analyses the user's goal in the context of bots and then formulates responses based on contextual analysis, much like a person would.
Programmers can provide bot training in natural language processing. By training it on a range of interactions and conversations, it will have a far broader base from which to evaluate and interpret queries.
Typical NLP tasks and techniques for creating chatbots
Parts of Speech Tagging
Parts of speech tagging is the process of assigning grammatical labels to words in text, indicating their role and function in a sentence.
NLP uses morphology, which describes the functions of individual words, to categorize each word referenced in the body text as a noun, adjective, pronoun, etc. This labeling is complex since words might have different functions depending on the context in which they are used. Classification can be difficult because terms like "bark" can mean both a barking dog and a tree.
Bag of Words
Bag of Words is a text representation model that counts the frequency of words in a document, disregarding grammar and order.
This NLP phrase refers to applying a model that creates a matrix of all the occurrences in a given text extract. It is a frequency table of all the words used in the text.
Once finished, you can utilize the frequency matrix to train classifiers. However, this approach forbids the use of contextual and semantic meaning.
Stop Word Removal
Stop Word Removal is the process of eliminating common words (e.g. "the", "and", "in") from text data to improve analysis accuracy.
Regular articles such as "a, the, to, etc." are eliminated because they add little to the text's content and merely act as filler. By removing frequent words that provide the reader with little to no information, stop-word removal simplifies NLP.
Lemmatization
Lemmatization, which unifies words with different spellings of the exact phrase after reducing them to their root form, is another helpful technique.
It successfully unifies synonyms ("huge" transformed to "large") and changes past-tense verbs into their present-tense equivalents (for example, "thought" becomes "think"). To distinguish between similar words, this standardizing procedure takes context into account.
Stemming
Stemming is a natural language processing technique used to reduce words to their base or root form, also known as the "stem". It is a form of linguistic normalization that helps to improve text analysis and information retrieval by grouping together words with the same stem.
For example, the stem of the words "running," "runner," and "runs" is "run." Applying a stemming algorithm would reduce all of these words to their common stem.
Tokenization
It involves successfully dividing a large body of text into smaller, ordered semantic units by segmenting each word, phrase, or clause into tokens.
Tokenization also allows us to remove punctuation, which makes segmentation simpler. However, as they are essential to morphology, parentheses, hyphens, and other punctuation are often required in academic writing. Naturally, only some languages function in the same manner.
Semantics
Semantics refers to the meaning of words, phrases, or sentences in a language and how they convey information.
To engage in NLP activities, we must be able to determine the true meaning of a text. Semantics is the study of a text's intended meaning. To fully comprehend this topic, linguists and computer scientists must continue their extensive research.
How to build a chatbot using NLP?
Here are the basic steps to follow when building a chatbot using NLP:
1. Business logic analysis
Determine the purpose and scope of the chatbot, what the chatbot is supposed to do, who its audience is, and what type of questions or tasks it will be handling.
The development team must complete this stage to understand the client's demands fully. Before evaluating business logic, a team typically needs to conduct a discovery phase, analyze the competitive market, select the essential qualities of their future chatbot, and then build their future product's business logic.
2. Channel and technology stack
Choose a platform or framework; several platforms and frameworks are available for building chatbots.
Using the Botpenguin platform as a base channel is preferable if you want to build a voice chatbot. On the other hand, Telegram, Viber, or Hangouts are the appropriate channels to use while developing text chatbots.
The most popular and often employed technologies for chatbot development are:
- Python- It is an architecture-building programming language for your future chatbot.
- Pandas- It is a software library in the Python programming language created for data manipulation and analysis.
- Twilio- Using its web service APIs enables software developers to programmatically place and receive phone calls, send and receive text messages, and carry out other communication tasks.
- TensorFlow– It is a standard library for tasks involving machine learning and neural networks.
- SpaCy – It is an open-source software program library for sophisticated NLP.
- Telegram, Viber, or Hangouts APIs– It is used to link your messengers or websites with a chatbot.
3. Development & NLP Integration
Development and NLP integration refer to incorporating natural language processing (NLP) technology into software development to create intelligent applications that can understand and respond to human language.
A machine learning chatbot must be built client-side and connected to the provider's API in two separate processes (Telegram, Viber, Twilio, etc.). We can use artificial intelligence with NLP to develop chatbots once the task ends.
4. Testing
We start asking the chatbot the questions we've taught it to respond to once it's ready. As usual, a few possibilities need to be looked at. Therefore, we may use manual testing. Testing helps ensure that your AI NLP chatbot is operational.
Is NLP necessary for chatbots?
Natural Language Processing (NLP) is a critical component of chatbots as it enables them to understand and respond to user inputs in a natural and human-like way. Without NLP, chatbots would be unable to process and interpret the language used by users, making them ineffective at holding meaningful conversations.
NLP allows chatbots to analyze the text provided by users, identify the intent behind the message, and extract relevant information. This enables them to generate appropriate responses relevant to the user's query or request. Additionally, NLP allows chatbots to learn from previous interactions with users, improving their ability to understand and respond to future messages.
Overall, NLP is an essential technology for chatbots, enabling them to provide effective customer service, automate tasks, and improve user engagement.
Conclusion
If the alternative requires simultaneously presenting the user with excessive options, NLP chatbots may be helpful. Simply asking your clients to speak or type their wishes could save confusion and annoyance on their part.
On the other hand, if implemented incorrectly, natural language processing chatbots can be a complete buzzkill and hurt rather than help your firm. If the user can complete the process with a few clicks, forcing them to write everything in will not simplify it.
So, preparing an implementation after learning about NLP technologies and readily available data is critical.
NLP-enabled chatbots improve communication by facilitating a seamless and productive conversation experience. So choose BotPenguin's NLP-based chatbots if you wish to use them in your company.
