5 Benefits of NLP Chatbots in the E-commerce Industry

NLU is something that improves the computer’s reading comprehension whereas NLG is something that allows computers to write. Before NLPs existed, there was this classic research example where scientists tried to convert Russian to English and vice-versa. Automatic summarization can be particularly useful for data entry, where relevant information is extracted from a product description, for example, and automatically entered into a database. Predictive text, autocorrect, and autocomplete have become so accurate in word processing programs, like MS Word and Google Docs, that they can make us feel like we need to go back to grammar school. Named entity recognition is one of the most popular tasks in semantic analysis and involves extracting entities from within a text. Entities can be names, places, organizations, email addresses, and more.

NLP techniques are used to enable machines to understand and generate human language, allowing them to comprehend and respond to user queries or commands. Natural Language Processing (NLP) has many real-world applications across various domains. It is widely used in sentiment analysis, where it analyzes public opinion from social media posts or customer reviews. Another application is machine translation, which involves translating text or speech between different languages. NLP also powers chatbots and virtual assistants, enabling them to interact with users in natural language. Information extraction is another important application, where NLP helps extract relevant information from unstructured text data such as news articles or research papers.

Which NLP Engine to Use In Chatbot Development

NLP chatbot’s ability to converse with users in natural language allows them to accurately identify the intent and also convey the right response. Mainly used to secure feedback from the patient, maintain the review, and assist in the root cause analysis, NLP chatbots help the healthcare industry perform efficiently. Natural language processing chatbots are used in customer service tools, virtual assistants, etc. Some real-world use cases include customer service, marketing, and sales, as well as chatting, medical checks, and banking purposes.

The use of Dialogflow and a no-code chatbot building platform like Landbot allows you to combine the smart and natural aspects of NLP with the practical and functional aspects of choice-based bots. BUT, when it comes to streamlining the entire process of bot creation, it’s hard to argue against it. While the builder is usually used to create a choose-your-adventure type of conversational flows, it does allow for Dialogflow integration. In fact, when it comes down to it, your NLP bot can learn A LOT about efficiency and practicality from those rule-based “auto-response sequences” we dare to call chatbots. Simply put, machine learning allows the NLP algorithm to learn from every new conversation and thus improve itself autonomously through practice. Unlike common word processing operations, NLP doesn’t treat speech or text just as a sequence of symbols.

Difference between NLP chatbots and rule-based chatbots

Now that you know the basics of AI NLP chatbots, let’s take a look at how you can build one. Another thing you can do to simplify your NLP chatbot building process is using a visual no-code bot builder – like Landbot – as your base in which you integrate the NLP element. Lack of a conversation ender can easily become an issue and you would be surprised how many NLB chatbots actually don’t have one. At times, constraining user input can be a great way to focus and speed up query resolution.

DialogFlow has a reputation for being one of the easier, yet still very robust, platforms for NLP. As such, I often recommend it as the go-to source for NLP implementations. Thus, the ability to connect your Chatfuel bot with DialogFlow makes for a winning combination. Previous to the acquisition API.ai was already one of the best sources for NLP, and since the acquisition has only increased in functionality and language processing capability.

Definition of an AI chatbot

Define the intents your chatbot will handle and identify the entities it needs to extract. This step is crucial for accurately processing user input and providing relevant responses. The quality of your chatbot’s performance is heavily dependent on the data it is trained on.

Read more about What is NLP Chatbot and How It Works? here.