Category: Chat

  • Why are AI voicebots better than IVR

    Man with glasses making a phone call
    Communication by voice is the most natural way of communication (source: unsplash.com)

    And how voicebots make calling contact centres valid again

    Gone are the days when a call to a company helpdesk provided you with direct contact with an operator or other live employee. Their places in companies’ contact and call centers are now occupied by technological solutions that save companies costs and staff capacity. With such a phone call, you will most often encounter the IVR (Interactive Voice Response) system today. Or, if you are more fortunate, a quicker and friendlier AI voicebot. But what are its benefits, and why you don’t have to worry about talking to artificial intelligence?

    Is it a robot, or is it a person?

    But what exactly is a smart voicebot? It is a solution that uses artificial intelligence, natural language processing, and machine learning technologies to communicate with the customer similarly to a human operator. Voicebots are similar to their more well-known relatives — chatbots. The most significant difference is that instead of written text it communicates in the form of sound, i.e. the human voice. Large call centers and helpdesks can no longer exist without it or IVR.

    Voicebots are on duty 24 hours a day and can independently deal with various tasks, gradually increasing the capacity of contact centers. Thanks to their capabilities, which are constantly improved, voicebots have received a lot of attention lately and are used in various companies globally. Both global corporations and startups — such as the Czech company Born Digital — are involved in its development.

    Green toy robot on the couch with other toys
    Sometimes you may not even recognize who you are talking to (source: unsplash.com)

    Older and slower sibling named IVR

    The biggest competitor, as well as a solution that is often mistaken with voicebots, are IVR machines. But they are slower and commonly less interactive and user-friendly. Communication with such an automaton takes place by voice too, but rather than a conversation, it is a voice-controlled passage through a prepared network of options.

    “It is the unattractiveness of IVR, that turned the focus towards text and chatbots. But with the advent of intelligent assistants like Alexa or Siri, people have regained their desire to communicate by voice. And just at that moment came the time of AI voicebots,” says Born Digital PR manager Marek Hadrbolec.

    The following lines show how these solutions are more capable than IVR and how they improve communication not only for companies but especially for people.

    Old and new airplane flying next to each other
    The speed of AI chatbot and IVR is very different (source: unsplash.com)

    The differences are obvious

    Imagine, for example, that your internet does not work at home. If you call technical support and your provider uses IVR, the conversation will look like this. First, you get to choose from several reasons why you’re calling, none of which are likely to be appropriate for your problem. Once you select one of the options, a slow, robotic voice will give you a couple of other options to refine your request. To choose one again you have to, for example, press a number key.

    Trending Bot Articles:

    1. Chatbot Trends Report 2021

    2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

    3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

    4. An expert system: Conversational AI Vs Chatbots

    Then the IVR will transfer you to the operator. After the annoying melody is over and the operator picks up the phone, you describe in your own words what trouble you have, after which the operator apologizes with the words that he will connect you to the right person. Another wait accompanied by an irritating melody, and only then the opportunity to finally solve your problem. At that moment, however, you do not have much patience left.

    Yellow toy robot watching sunses in nature
    The result may seem really far away while talking to an IVR (source: unsplash.com)

    If your ISP has invested in an AI voicebot, the whole call will be completely different. First, the system introduces itself to you in a pleasant, almost human voice and asks about your problem.

    “You do not choose from the preset options, but you describe what technical difficulties you face in your own words. A properly developed voicebot will understand your speech and transform it into meaningful information about your situation,” explains Born Digital director Tomáš Malovec.

    The next step will probably be for the voicebot to suggest one of the solutions that it evaluates as accurate and give you instructions on how to apply it. If his suggestion doesn’t help, the voicebot can provide another. If the fundamental solutions are not working, and the technology gets into trouble, it does not force you to repeat the tasks or prolong the call. Instead, it redirects you to an experienced operator who will be competent to solve your problem. So you don’t have to worry about further switching.

    Three man on roller skates racing
    The results of this competition are obvious (source: unsplash.com)

    The winner takes it all

    Now it is clear why AI voicebots revive customers lost confidence in voice communication. The main reason is that the voicebot, just like a skilled human operator, adapts to your language, while the IVR cannot adapt this way. Voicebots are not alive, but they still deliver natural conversation, which is interactive and straightforward. Thanks to this, it is much easier to reach the core of the problem. Compared to IVR, voicebots are also much more independent and able to solve problems on their own or even learning to solve new ones.

    “The bots often collect anonymized data about the conversation flow. With these rich sources of information, it is easy to improve their conversation system to be even more productive,” adds Hadrbolec.

    Voicebots defeat IVR even in difficult situations. Thanks to understanding your problem in more detail, it will much more likely connect you to the right operator. So if your call is answered by a voicebot, don’t worry. The worst thing that can happen is that after a short period of uncertainty, you end up in the hands of an experienced human operator who will solve your problems.

    Don’t forget to give us your 👏 !


    Why are AI voicebots better than IVR was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Energy Poverty Chatbot

    Hey guys!

    I am developing a chatbot to assist individuals who are struggling to afford their energy bills find solutions to their problems. The chatbot will provide information on how to reduce energy use in the home, subsidies/ grants & programs that can help, and ways to avoid electricity disconnection.

    A brief survey has been created in order to identify input cases (the message a user sends to the bot). I need to collect as many input cases as possible in order to train the bot to understand the user’s intent. I would greatly appreciate any feedback. Thanks in advance!!

    Survey link: https://docs.google.com/forms/d/e/1FAIpQLScuQFAdfnDycfzQi-TEDb5lmFksrPAVQ5UaHuiJIx-Wx_XRQQ/viewform?usp=sf_link

    submitted by /u/_Mar_Berk_
    [link] [comments]

  • How AI-powered solutions can help your business craft superior omnichannel customer experiences

    A great omnichannel support strategy bestows your customers with consistent, seamless support across platforms and agents with a singular view on customer insights

    How AI-powered solutions can help your business craft superior omnichannel customer experiences

    One of the prickiest problems enterprises face with their string of legacy systems employed to craft integrated, omnichannel customer experiences is, unfortunately, not being able to do exactly that.

    As much as organisations try to be “customer first” across their various self-service and assisted service channels, without a unified approach to managing their multichannel and cross-channel journeys, they fail to manage the customer’s experience effectively.

    Customers are frustrated when they transition from self-service to assisted service and have to re-explain their needs. Agents are frustrated when they cannot find critical customer information in real-time and lack the knowledge and context to deliver low-effort experiences. A lot of times if a customer calls, they have no record of the email the same customer had sent a few days ago.

    Fragmented customer experience, missed sales opportunities, and reduced operational efficiency

    Why the multisystem-multichannel approach doesn’t help craft omnichannel customer experiences

    Even as businesses have technologies and processes in place that are meticulously knitted together to enable customers to connect with the company via a channel of their choice, they don’t generate desired business outcomes.

    Instead, these infrastructures only create problems for the customers and the service representatives alike. And far from lending any trace of customer satisfaction, the multiple systems, typically managed in silos, sink the bottom line in operational costs and revenue.

    The reasons being:

    1. The multiple legacy systems prioritise customers based on the interaction channel they are using and not on the value or opportunity that lies ahead.
    2. There are, often, multiple resources working on a single customer problem through various different channels resulting in dissonance and loss of common ground.
    3. Even as the customer stands to receive a lot of information from myriad sources, most of it is conflicting and chafes confidence and trust in the brand.

    Trending Bot Articles:

    1. Chatbot Trends Report 2021

    2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

    3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

    4. An expert system: Conversational AI Vs Chatbots

    What defines a winning omnichannel customer experience?

    Convenience, consistency, and value are the hallmarks of real, omnichannel customer experience. Perfect omnichannel experiences are guided trails that frictionlessly provide customers with exactly the resources they want, when they want it, through the touchpoint they demand it.

    Customers value real-time, personalised interactions, finely tailored to their buying preferences, transaction history, and user behaviours, and cherish support experiences that inform and empower them. When businesses are agile enough to be able to quickly adapt to the ever-changing consumer sentiment and channel preferences across digital and voice and provide seamless 24/7 support with instant resolutions and high first-time resolution rates, they can be said to have finally orchestrated truly winning omnichannel strategies.

    Advantages of omnichannel customer experiences

    How AI-powered solutions can help your business craft superior omnichannel customer experiences

    01 Enhanced customer satisfaction

    Consistent, personalized engagement with the customer across all channels, in the context of the full relationship and active customer journeys, marks up customer satisfaction

    02 Improved sales conversions and revenue

    When incoming requests are triaged to the best agent based on customer segmentation, customer journey, recent interaction history, or agent skills, close, up-sell, and cross-sell rates increase significantly and revenue sees a good rise

    03 Ensured first-contact resolution while repeat callers and transfer rates go down

    When the right agents are routed select requests upon leveraging the omnichannel customer interaction history and journey, they are empowered to resolve the customer problem upon first contact and boost customer happiness

    04 Reduced average handle time

    Every time an agent starts out to attend to a customer query, they receive a screen popup detailing the customer’s consolidated contact history and context of previous interactions across channels. This helps the agent handle the request with greater speed, efficiency, and personalization

    05 Reduced duplicate processing and inefficiencies

    Good omnichannel systems automatically consolidate duplicate customer messages through different channels into a single messaging for the contact centre agent to reduce both rework and the potential for inconsistent responses.

    How you can craft truly omnichannel customer experiences

    Understanding the customer is paramount to creating an omnichannel strategy that can generate rewarding outcomes for your customer as well as your business. To begin with, you need to have a comprehensive knowledge of what journeys your customers are on, where they are in that journey, and what they are trying to achieve to be able to successfully guide them to the best result.

    Leveraging the right technology is critical to creating omnichannel customer experiences because most businesses lose track of the customer journey when they contact them through more than one channel. And 66% of consumers have used at least three different communication channels to contact customer service, as per Microsoft.

    So adopting the right collaborative tools for customer support is indispensable to the success of your enterprise, with respect to being able to provide a single, unified, end-to-end system of engagement and deliver seamless, consistent and personalized customer interactions across the multiple fronts like web, mobile, social media, and the array of different support channels, throughout the full lifecycle of customer journeys,

    We, at Enterprise Bot, have just the AI-powered tools that can provide omnichannel customer query resolution and engagement capabilities by proactively and efficiently managing customer’s previously random journeys across channels.

    How AI-powered solutions can help your business craft superior omnichannel customer experiences

    Our proprietary chatbot can collate data from various different support channels into a single resource for agent use and empower your service staff with generally limited bandwidth.

    The bot automatically stores reserves of customer data while they interact with them through various channels. This data, tracing the purchase history, inquiry frequency, number and types of clicks, etc., of the customer, helps the chatbot draw out the customer’s profile with utmost precision.

    Leveraging these detailed customer insights, the agents can stay up-to-date with the customer’s imprints in real-time to be able to solve even the complex problems speedily and provide genuinely superior omnichannel customer experiences.

    Armed with pre-built integration capabilities, our enterprise-ready AI chatbot quickly blends with existing operations infrastructure, integrating into any core software with or without using APIs, and quickly gaining in-depth customer insight for responding meaningfully and completely.

    It is easy to integrate with any website, app, commonly used enterprise software like Genesys, Guidewire, Salesforce, UI Path, SAP, and popular support channels like Skype for Business, Facebook Messenger, Twitter, Slack, Genesys chat, Intercom, and more, to automate around 80% of customer queries, improve client satisfaction by around 15%, and boost sales by at least 25%.

    How AI-powered solutions can help your business craft superior omnichannel customer experiences

    Another one of our CX solutions, our proprietary Email Response Automation software, ERA, also quickly integrates with commonly used customer support channels like Gmail, Exchange Web Service, Outlook, Zendesk, Freshdesk, Genesys, Salesforce in a few, quick clicks, to automatically create tickets from emails, triage them to the right team, and send instant responses to customers. You can also integrate it into your core softwares using APIs or even legacy systems with no APIs to enable the bot to have deeper client insight for email classification and routing.

    The solution ensures 90% faster routing with over 85% accuracy and increased cost efficiency by over 75%. Automated omnichannel request routing and skills-based scheduling of workforce across all channels reduce time swallowed in manual triage and scheduling. It also improves agent occupancy via real-time forecasting of contact volume throughout the day.

    Visit https://enterprisebot.ai/ to know more about our omnichannel AI-powered solutions across chat, email, and voice. For any queries, mail contact@enterprisebot.ai

    We are here for you, 24/7.

    Don’t forget to give us your 👏 !


    How AI-powered solutions can help your business craft superior omnichannel customer experiences was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • What is changing in the Ecommerce business?

    Ecommerce is changing drastically. The impact is technology is so much in the e-commerce sector that it has changed and modified the way technology works.
    Let’s take an example of how eCommerce and chatbot technology are working all together to create an amazing service hence ultimately. Would like to know much more about how much of an impact and difference technology has made in this sector.

    submitted by /u/deepyanti199
    [link] [comments]

  • [Academic Survey] Trustful Chatbots during crises – a COVID-19 case study (German, all demographics)

    Hi everyone! My name is Lukas and I am looking for people who speak German to participate in my university research. As the title suggests, my research team and I are researching the way in which chatbots can generate the users’ trust when used in a crisis situation. You will chat with an actual chatbot and answer some questions afterwards.

    The study will take about 15-25 minutes (depending on how long you want to play around with the chatbot).

    Link: https://survey.proco.uni-due.de/668117?lang=de

    Thanks to everyone who participates!

    All the best,

    Lukas (V0KUN)

    submitted by /u/V0KUN
    [link] [comments]

  • Know more about yourself with survey-based method and AI Chatbot (18+)

    Individuality matters. Have you ever wondered what you are really like, for example, what your unique personality is and what are your unique strengths or weaknesses?

    At Indiana University, we are investigating how artificial intelligence (AI) can provide insights into your personality and help you better discover yourself. We are comparing the AI method to a traditional survey-based method. If you are interested in knowing more about yourself and helping us evaluate the two methods, we invite you to participate in our study.

    Please go to the survey.

    It will take about an hour to do the study. To say our thanks, you can choose to provide your email address at the end of study to be entered in our drawing to win a $25 Amazon gift card. If you do not want to enter the drawing, there is no need to provide your email and you can do the study anonymously.

    submitted by /u/make_chatbot
    [link] [comments]

  • Create a WhatsApp Bot in 3 easy steps

    WhatsApp’s massive growth in popularity over the past few years has created many opportunities for businesses. Companies around the world are currently discovering the power of the WhatsApp Business app. It allows you to send notifications and messages using a WhatsApp chatbot, which is a great way to increase and personalize customer engagement.

    WhatsApp has become one of the most popular messaging applications globally, with over 1.5 billion users, and the latest launches of Company Profiles and Business Accounts open up a new room for companies to connect with people all over the world.

    What is a WhatsApp Bot?

    WhatsApp chatbot is a software program running on the encrypted WhatsApp platform. WhatsApp Bot is simply an automated chat system connected to a WhatsApp phone number linked to your business.

    How to create a Chatbot for WhatsApp?

    Companies can create WhatsApp bots for customer service and notification delivery using the WhatsApp Business API. So in this article, I will show you exactly how to build a WhatsApp bot and tell you everything you need to deploy it on WhatsApp using Kommunicate and Twilio.

    Prerequisites

    You will need a Twilio account for setting up your WhatsApp business API and a Kommunicate account for deploying the chatbot to your WhatsApp and manage customer conversations. Both of these platforms have free trials.

    Step 1: Building a chatbot

    Kommunicate provides a bot builder tool called Kompose, where you don’t need to write a single line of code. You can create AI-powered chatbots, deploy them to your website and apps, and even add automated human takeovers if they fail. Please refer to this for detailed instructions on how to build a chatbot.

    Kommunicate also lets you integrate with other third-party bot builder platforms such as Dialogflow, Amazon lex, IBM Watson, and custom bots.

    First, you need to login into your Kommunicate dashboard. If you do not have a Kommunicate account, you can for free.

    🚀 Bonus: If you want a pre-built chatbot? Here’s where to find a bunch of them!

    Trending Bot Articles:

    1. Chatbot Trends Report 2021

    2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

    3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

    4. An expert system: Conversational AI Vs Chatbots

    Step 2: Configuring Whatsapp

    Here, we use Twilio for WhatsApp integration, and You must complete the Twilio sign-up process before starting the integration with Kommunicate. Please sign up to Twilio here if not already registered.

    Prerequisites

    Request access to enable your Twilio numbers for WhatsApp

    To do the integration on Kommunicate, one needs to have the WhatsApp number that Twilio provides. To get a WhatsApp number, fill out Twilio’s Request Access form with accurate and up-to-date information, including the Facebook Business Manager ID. Please refer to Twilio’s documentation for more information

    Submit a WhatsApp Sender request in the Twilio console

    Once you fill the Request Access form and submit it, you will receive the Twilio pre-approval email. Check out this referral link for the same information.

    Then, in the Facebook Business Manager console, allow Twilio to send a message on your behalf. Also, submit your Facebook Business Manager account for Business Verification.

    Note: Once you complete all the above steps, you will get the Whatsapp number to add it in the Kommunicate integration section. You can find the Whatsapp enabled number in Whatsapp enabled senders section of the Twilio console. Refer to the following screenshot for more details.

    Twilio — WhatsApp Enabled Senders

    Step 3: Integrating WhatsApp with Kommunicate

    The following instructions will help you connect Kommunicate with Twilio and thus with WhatsApp.

    • Copy Account SID, Auth Token from Twilio console, and WhatsApp Enabled Number from Whatsapp Enabled Senders section and add it in Kommunicate integration section.
    • Integrate with Kommunicate as shown below.
    • Once integration with Kommunicate is finished, visit the WhatsApp Enabled Senders section on Twilio and click on Edit Sender.
    • Now add the Webhook URL and select the HTTP POST. Also, add a URL in the status callback URL textbox and Save/Update the WhatsApp sender. URL

    Final Step: Test your WhatsApp Bot

    Now Kommunicate is successfully integrated with the WhatsApp account, try sending messages to the number linked to the account. These messages will be sent to Kommunicate chat as well. If the chatbot is already integrated, the bot will start replying to your WhatsApp chat queries.

    This is how the conversations will look like in the Kommunicate dashboard.

    Limitations

    1. You can only send and receive specific media files. See more info here.
    2. If more than 24 hours have passed since the last user message, only template messages will be allowed.
    3. WhatsApp does not support rich message responses from the bot, so they will not be received by the end-user.

    Why use Kommunicate for your WhatsApp Bot?

    Although it requires Twilio to integrate your chatbot with WhatsApp, Kommunicate is an effective option to run fully automated customer support.

    Kommunicate allows you to manage customer conversations from WhatsApp, Facebook, Live Chat, Chatbots, and Emails. Additionally, chatbots and humans can work in perfect sync using Kommunicate’s customer support automation platform.

    Hence I would recommend using Kommunicate’s WhatsApp integration if you like to manage your customer support well. If you plan to just send WhatsApp notifications to your users and not a two-way dialogue, I recommend using other services.

    Although the Kommunicate features discussed in this article are powerful tools for creating WhatsApp bots, they still only scratch the surface of what you can achieve with the Kompose chatbot and its integrations. So I highly recommend that you start exploring Kommunicate.

    It’s time to leave the programmable SMS in the past and revolutionize customer conversation. Happy Kommunicating!

    Don’t forget to give us your 👏 !


    Create a WhatsApp Bot in 3 easy steps was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • Automatic Utterances Clustering for Chatbots

    A custom python model which automatically clean up the utterances and generate the possible clusters and slots, making it easier for you in merging those clusters together to form as many intents as you need to feed a chatbot?

    To build a chatbot, I feel that the high-level steps we usually take are the following.

    • Collect Utterances and Cleanup — This includes removing unwanted utterances, email ids, numeric and special characters
    • Create Intents — Analyse the utterances and start clustering the related utterances together to form intents.
    • Create Slots — Identify associated words in the utterances to form slots/entities.
    • Select NLP — The core component that interprets the user’s utterance and converts that language to structured inputs (Intents and Slots) the system can process. The top NLPs available in the market such as AWS Lex, Microsoft LUIS or Google Dialog Flow, etc…
    • Build — Train the chatbot with utterances and attach slots
    • Publish — Publish to get an endpoint of the chatbot
    • Write Response Logic — Write backend logic in any programming language of your interest, to respond to the user’s utterance.

    The most important role among the above 7 steps is played by your NLP, being intelligent enough to understand what the context of a user is. So, it is imperative to capture a variety of example utterances and accurately classify them into intents to provide better accuracy in predicting the new utterances. That means you have to spend more time analyzing the utterances, removing duplicates, special characters, and misrepresentations, before classifying them.

    How complicated is it to create Intents and Slots?

    Let’s take some examples of booking related utterances,

    Is my booking confirmed?
    I need to book a flight from Bangalore to Paris
    Can I get the top 5 hotels list for accommodation?
    I have not got any confirmation of my booking
    Which is the best locality to stay in Paris?
    How can I book a ticket to Paris?
    I have to travel to Amsterdam tomorrow
    Which is the closest hotel to Paris airport?

    Stop here for a while without scrolling below and think how many intents and slots can be created? (Note how much time it took for this activity to complete)
    The “context” of each utterance must be carefully understood and separated based on how similar or dissimilar one is to others.
    Here’s how I feel they can be classified at a high level-

    Trending Bot Articles:

    1. Chatbot Trends Report 2021

    2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

    3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

    4. An expert system: Conversational AI Vs Chatbots

    I need to book a flight from Bangalore to Paris
    How can I book a ticket tonight to Paris?
    I have to travel to Amsterdam tomorrow

    Can I get the top 5 hotels list for accommodation?
    Which is the best locality to stay in Paris?
    Which is the closest hotel to Paris airport?

    Is my booking confirmed?
    I have not got any confirmation of my booking?

    > The first cluster is talking about booking a flight for a location
    > Second cluster talks about hotels inquiry and
    > The third cluster collecting the common utterances which can be related to any bookings.

    W.R.T slots,
    > SourceLocation- Bangalore
    > DestinationLocation- Paris/Amsterdam
    > Day- tonight, tomorrow

    So, there can be 3 intents and 3 slots. It looks easy right!
    Now, what if we have 10,000 raw utterances? How long do you think you need to generate slots and intents?
    It just isn’t finishing here. Most of the time, the utterances that you get would not be that clean. As I said before feeding them to your NLP you need to remove unwanted stuff from the data sets.

    Even if you have a good amount of experience in building chatbots, you have to spend more time preprocessing the utterances. I can say it would take weeks for an experienced and months for an average or naive.

    So, what do we do?

    What if there is a program that can reduce your effort in doing this?
    Reduce your effort of these 3 below

    • Collect Utterances and Cleanup
    • Create Intents
    • Create Slots

    Let me think! Basically, we need something which automatically cleanup, compares one sentence with another to group sentences that are more similar (greater than 80%) to each other, and identifies slots.

    In more technical terms,

    1. First, sentences to be preprocessed
    2. then, convert sentences to vectors (Embedding) and compare vectors and group the similar ones
    3. later, identify the most frequently occurring words contextually in the entire corpus

    I spent a lot of time with trial and error seeing if there’s something that can help me cluster the utterances without asking me how many clusters are needed. Because I don’t have any idea how many clusters or intents do I need.
    I found a few models that were doing the job but, due to lack of data, there was a lot of mix and match. They work really well on a huge amount of like millions of utterances. But, my dataset is not that huge and the scope is limited. So, I finally thought let’s write a custom model that can fit my requirements.

    How is the custom model implemented?

    This custom model is inscribed in python.

    1. First, we need to preprocess the data

    Lowercase, Remove email ids and URLs, Remove special characters and numbers, Remove stopwords, and finally Lemmatize (Identify and replace the base form of a word) if you are interested.

    2a. Convert sentences to vectors

    This and the preprocessing are the most crucial steps in the entire ML automation process. I assume here that you know, why we convert sentences to vectors. There are many ways to do this and I would choose a few of them such as TFIDF, Word2Vec, and BERT. You can go through a few tutorials and articles to find out how do they work.

    2b. Grouping the similar sentences

    Here you need a logic that compares the sentences and group (actually compare the vectors).
    There are two most popular formulas used to find the distance between two vectors
    a. Cosine Similarity
    b. Euclidean Distance

    3. Contextually occurring similar words

    We will use our basic logic to figure out how words appear between their neighbouring words and how frequently they appear with the same neighours.

    Let’s run the code

    It takes an excel file that contains the list of utterances as input and returns another excel file containing the most possible slots and clusters.

    To begin, clone the master branch or download it from the GitHub location.

    https://github.com/machinelearning01/text-clustering

    Go to the test1.py file and pass the excel file path in the parameters.

    "excel_data": input_data("<excel file path>"),

    Run the code and see the results in excel ready!

    $ python test1.py

    Conclusion

    For a single problem, there’ll always be different ways. This custom model saves weeks of time from looking again and again at the same utterances that it is not sure where the chatbot accuracy ends up. So, I suggest you look at the model and run through it to see the result variations you’re getting, train, and test the chatbot and let me know your comments.

    Don’t forget to give us your 👏 !


    Automatic Utterances Clustering for Chatbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.