This post was made public as part of the Data Science Blogathon
According to Accenture, “the 57% of companies agree that chatbots offer a higher return on investment with minimal effort”.
Table of Contents :
1. What is a chatbot?
2. A dive into the types of chatbots
3. What are the best platforms to build a chatbot?
4. What are the best frameworks to build a chatbot?
5. The algorithm to build a Chatbot.
6. Tips to follow before creating your first chatbot
7. The 5 main data sets for your chatbot
1. What is a chatbot?
The first step before learning something is that we need to know what exactly we are going to learn. Then, let's start by understanding “What is a Chatbot?”
A chatbot is a software that interacts with humans through various means such as voice, text, gestures, etc. A chatbot with the power of AI domains like NLP (natural language processing) enables smart chats and more natural conversations between humans and technology.
Chatbots were widely used for customer services, such as automatic responses and automated targeting. But currently chatbots are being used in various sectors such as the educational field, personal services, travel assistant services, medical fields, etc. The important reason to adopt chatbots is because they make the business more efficient and improve the customer experience, what a real fuel for any business.
Virtual assistants like Alexa, Siri and chatbots on Facebook Messenger, Telegram, WhatsApp or any web portal fall under the hood of chatbots.
2. A dive into the types of chatbots
Chatbots are classified into 2 (From) types: –
2.1. Based on linguistics / rule-based:
This category of chatbots simply follows the rule or algorithm to answer any question / query made by the user. These are simple bots that can only answer the set of questions that have been pre-programmed within it.. Therefore, the bot could not respond more naturally and the intelligence of these bots was totally dependent on the programmer who designed them. It is created through literal literal encoding (in general logical if / else / then).
There are many advantages to using rule-based chatbots: they are not expensive, easy to handle, easy integration, highly safe and responsible, can include interactive items such as images, videos, etc.
2.2. Chatbots de IA:
Artificial intelligence chatbots that use machine learning to be more general specific are created through the NLP technique (natural language processing), understand the context and intent of a question before answering or taking any action. These chatbots generate their own answers to more complicated questions using natural language answers.. The more you use and train these bot, the more they learn and the smarter they become with almost real types of responses.
The advantages here are: They are intelligent, learn from information and experiences, relatively more natural responses, a broader range of decision-making skills, etc.
3. What are the best platforms to build a chatbot?
Chatbot platforms are nothing more than a platform to create chatbots and develop them. Chatbot platforms are the best place for beginners to start. These platforms have a simple and easy-to-use design and we do not need any coding knowledge, it is simply a drag and drop function. All that would need to be done is to form the flow or algorithm of how this chatbot will work.
Again, Chatbot platforms are classified into two types: –
3.1. Development platforms:
Chatbot development platforms enable you to create a chatbot with / no coding in minutes. Can design, build, test and implement chatbots with the platforms I have listed some development platforms below.
- Chatfuel
- Botsify
- Manychat
- Bottr
- Flow Xo
- api.ai
- motion.ai
- Botstar
3.2. Publishing platforms:
It is a platform where the user can enter and use the chatbot. Some widely used platforms are: –
- Loose
- Amazon Echo
- Skype
- Delivery courier
- Viber
- Web portal
- Telegram and etc.
4. What are the best frameworks to build a chatbot?
Chatbot Frameworks are a kind of SDK that enables developers to build using NLP, NLU and various other cutting edge techniques. Frames offer basic building blocks as intent, the context, entities and conversation layout, according to which developers need to create bots through coding. Unlike platforms in Frameworks, no drag and drop functions, there is no predefined flow or any kind of template.
- Dialog flow de Google
- Lex from Amazon.
- Bot Framework de Microsoft (MBF)
- IBM Watson
- WordPress
- Chatterbot
- Pila RASA
5. The algorithm to build a Chatbot.
- Indicate the purpose of the chatbot: What kind of services? What problem to tackle? What is the purpose of the chatbot's existence?
- Is it going to be a rule-based chatbot / AI enabled?
- Select the right platform: It depends on your time, knowledge and budget.
- Choice of frame: Choose what type of technique or technology to use, depends on programming knowledge and experience, How complex will your chatbot be?
6. Tips to follow before creating your first chatbot
- The chatbot should sound too robotic and shouldn't be too formal.
- Follow the algorithm mentioned previously.
- Keep repeating your chatbot.
- Get coders and developers in touch, not just to guide you, but also to get valid feedback and ideas.
- Be aware of your time and budget.
7. The 5 main data sets for your chatbot
For robust ML and NLP models, training the chatbot dataset with correct big data leads to desirable results. Chatbot data sets require an exorbitant amount of big data, trained using various examples to solve user query. Despite this, training chatbots with bad or insufficient data leads to unwanted results.
- Natural questions: NQ is the data set that uses naturally occurring queries and focuses on finding answers by reading a whole page, instead of relying on extracting answers from short paragraphs.
- NPS Chat Corpus: The NPS Cat Corpus is part of the Natural Language Toolkit distribution (NLTK). Includes both the full NPS Chat Corpus and various modules to work with the data.
- Yahoo language data: Yahoo language data is a form of question and answer dataset selected from responses received from Yahoo!. The dataset consists of the anonymous bipartite membership graph only and does not contain information on users, groups or discussions.
- Team: Stanford Question Response Data Set (SQuAD) is a reading comprehension data set, consisting of questions asked by crowd workers in a set of Wikipedia posts, where the solution to each question is a text segment, O lapse, of the respective reading passage, or the question could be unanswerable.
- ClariQ: The ClariQ The challenge is organized as part of the EMNLP Workshop on Search-Oriented Conversational AI (Velcro) in 2020. This is a form of conversational AI systems and series, with the primary purpose of returning an appropriate response in response to user requests.
Hope this elaborate guide has given you insight and vast knowledge about chatbots. Soon, in the future, we will also write about how to build a chatbot. Stay tuned!
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