Analysis of the Readability Metric Results in Almeta News Feed

In this post, we’re analyzing the results returned by the readability metric in our news feed. If you haven’t checked our post about “How to measure the readability of a text?” before, you can read about it here. How Are We Measuring the Readability? The main part of analyzing a metric is to know how does it work. In the current version, we’re depending on the AARIBase metric for measuring the readability. So, let’s have a look first on how does AARIBase work. Here’s the AARIBase formula: AARIBase = (3.28 × NOC) + (1.43 × ACW) + (1.24 × AWS) … Continue reading Analysis of the Readability Metric Results in Almeta News Feed

Clickbait Detection Using Word2Vec Representation

In a previous article, How to Detect Clickbait Headlines using NLP? We introduced the task of clickbait detection and explored how it can be modeled within the domain of machine learning and NLP. If you are not familiar with the concept of clickbait detection, make sure to review it before continuing. In this post, we’re building a classifier for clickbait detection in the news headlines depending on a pre-trained Arabic Word2Vec model and we’re validating this solution. If you are not familiar with the Word2Vec concept you can refer to this Wikipedia article for more information. News Headlines Representation In … Continue reading Clickbait Detection Using Word2Vec Representation

Google’s AutoML Overview

In this post, we are exploring how Google’s AutoML can help us in Almeta in developing automatic Arabic language processing tools. Before start if you are not familiar with the term AutoML you can refer to our previous post on this topic. Who is Google AutoML for? and When to Use It? The targeted audience by Google’s cloud autoML are people who have limited knowledge in machine learning. The main goal of this cloud service is to let the user build his own AI model that is tailored to his business needs, if the provided services by Google’s AI API … Continue reading Google’s AutoML Overview