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What is document classification, and how can machine learning help? | by Shaip | Aug, 2022

30Aug

By ShaipNo Comments

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What is document classification, and how can machine learning help? | by Shaip | Aug, 2022

Imagine this: you head into a standard bookstore where pieces are supposed to be classified as genres — like thriller, romance, science fiction, and more. You want to pick Andy Weir’s Hail Mary — a novel with thriller/mystery and science fiction elements.While the book choice seems on point, the question is: which genre should you head towards? The book can be on the science fiction shelf or on the thriller counter. It can be anywhere. And that is when the manual document classification becomes troublesome.Sweating already? Fret not, as machine learning is here to help. Not to throw shade at the manual document classification, but they can be tedious if you plan on looking at a world outside books — including inventories and databases.Yet, document classification with machine learning can ...

Using Transfer Learning with Word Embeddings for Text Classification Tasks | by Manuel Gil | Jul, 2021

8Aug

By Manuel GilNo Comments

ai bot, ai chat, ai chatbot, best chatbot, chatbot, chatbot ai, chatbot app, chatbot online, chatbot website, conversation with ai, creating chatbots, robot chat, tensorflow, text-classification, transfer-learning, word-embeddings

Using Transfer Learning with Word Embeddings for Text Classification Tasks | by Manuel Gil | Jul, 2021

When we are working with computer vision tasks, there are some scenarios where the amount of data (images) is small or not enough to reach acceptable performance. In addition, dealing with image data and Convolutional Neural Networks (CNN) is expensive in terms of computational power.Due to the issues aforementioned, in most cases it is convenient to use a technique called Transfer Learning, which consists of using models trained with millions of images, to improve the performance during the training process. We can implement this technique with Natural Language Processing (NLP) tasks, but instead of using pre-trained CNN models, for text classification, we are going to use pre-trained Word Embeddings.When we have so little data available to learn an appropriate task-specific embedding of ...

Evaluating classification models. Accuracy, Precision and Recall. | by Manuel Gil | Jul, 2021

14Jul

By Manuel GilNo Comments

ai bot, ai chat, ai chatbot, best chatbot, bots, chatbot, chatbot ai, chatbot app, chatbot online, chatbot website, chatbots, conversation with ai, creating chatbots, data-science, machine-learning, model-performance, robot chat

Evaluating classification models. Accuracy, Precision and Recall. | by Manuel Gil | Jul, 2021

In this article, I am going to delve into some metrics used to measure how well classifiers do their job. So after reading this article you will know how to evaluate classification models and know the difference between the different metrics that we can encounter evaluating classifier models.In the image shown above, we can see a classification problem. How can we know if this model is good or bad? Let’s delve into this in the following paragraphs.The goal of each classifier is to assign one label to one input according to their characteristics, in other words, classifiers can distinguish the instances belonging to different categories. But how can we measure how well a classifier performs this task?The first idea that comes to our minds is probably to calculate the ratio between the corre...

Hands-on for Toxicity Classification and minimization of unintended bias for Comments using classical machine learning models | by Anurag Maji

2Feb

By Anurag MajiNo Comments

ai bot, ai chat, ai chatbot, best chatbot, chatbot, chatbot ai, chatbot app, chatbot online, chatbot website, classification-algorithms, conversation with ai, creating chatbots, eda, kaggle-competition, machine-learning, nlp, robot chat

Hands-on for Toxicity Classification and minimization of unintended bias for Comments using classical machine learning models | by Anurag Maji

In this blog, I will try to explain a Toxicity polarity problem solution implementation for text i.e. basically a text-based binary classification machine learning problem for which I will try to implement some classical machine learning and deep learning models.For this activity, I am trying to implement a problem from Kaggle competition: “Jigsaw Unintended Bias in Toxicity Classification”.In this problem along with toxicity classification, we have to minimize the unintended bias (which I will explain briefly in the initial section).sourceBackground:This problem was posted by the Conversation AI team (Research Institution) in Kaggle competition.This problem’s main focus is to identify the toxicity in an online conversation where toxicity is defined as anything rude, disrespectful, or othe...

IBM Watson Assistant provides better intent classification than other commercial products according to published study | by Nicolo Cosimo Albanese | Jan, 2021

17Jan

By Nicolo Cosimo AlbaneseNo Comments

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IBM Watson Assistant provides better intent classification than other commercial products according to published study | by Nicolo Cosimo Albanese | Jan, 2021

And how the better performances of IBM Watson Assistant tackle the most common pain points in the adoption of a conversational solutionPhoto by Alex Knight on UnsplashToday, virtual assistants (or chatbots) represent a globally recognised booming trend:With regards to chatbots, which are in many ways the most recognisable form of AI, 80% of sales and marketing leaders say they already use these in their CX or plan to do so by 2020¹.25 percent of customer service operations will use virtual customer assistants by 2020².By 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%³.It is clear that chatbots are here to stay.Their limitless potential in terms of business versatility — is there any business t...