Trivia is any fact about an entity which is interesting due to its unusualness, uniqueness, unexpectedness or weirdness. In this paper, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their interestingness as trivia. At the heart of our system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based features. Our ranking model is trained by leveraging existing user-generated trivia data available on the Web instead of creating new labeled data. We evaluated our system on movies domain and observed that the system performs significantly better than the defined baselines. A thorough qualitative analysis of the results revealed that our rich set of features indeed help in surfacing interesting trivia in the top ranks.

Target-dependent sentiment analysis on Twitter has attracted increasing research attention. Most previous work relies on syntax, such as automatic parse trees, which are subject to noise for informal text such as tweets. In this paper, we show that competitive results can be achieved without the use of syntax, by extracting a rich set of automatic features. In particular, we split a tweet into a left context and a right context according to a given target, using distributed word representations and neural pooling functions to extract features. Both sentiment-driven and standard embeddings are used, and a rich set of neural pooling functions are explored. Sentiment lexicons are used as an additional source of information for feature extraction. In standard evaluation, the conceptually simple method gives a 4.8% absolute improvement over the state-of-the-art on three-way targeted sentiment classification, achieving the best reported results for this task.

We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled. When Julia was conceived in 2009 at MIT the goal was to solve a problem that still exists: the need to use two (or more) languages, one for high performance (C or C) and another that made programming complex systems a more pleasant experience (the Python example).

Recommender systems have found enterprise application by assisting all the top players in the online marketplace, including Amazon, Netflix, Google and many others. These systems are the decision support systems that make the personalisation process better as well as smoother. It predicts and estimates the content of user preferences by extracting from various data sources such as previous database, data history, among others. Here, we have listed the top eight open-source recommender systems in Python, in no particular order, that you must try for your next project. About: LensKit is an open-source toolkit for building, researching, and learning about recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education.

There are many machine learning frameworks. Given that each takes much time to learn, and given that some have a wider user base than others, which one should you use? Here we look briefly at some of the major ones. In picking a tool, you need to ask what is your goal: machine learning or deep learning? Deep learning has come to mean using neural networks to do, for the most part it seems, image recognition.

The Cortana Analytics Suite (CAS) is made up of different components in Azure, allowing users to custom build an analytical application to suit a wide range of analytics scenarios such as real-time recommendations, customer churn forecasting, fraud detection, and predictive maintenance just to name a few. In this post, we'll look at four problems with traditional data warehouses and show how the new Azure SQL Data Warehouse (part of the CAS) overcomes them and makes analytics available to organizations of all sizes. When developing a new data warehouse, one of the first steps is sizing and commissioning hardware requirements. However, sizing a data warehouse for both storage and processing can be difficult as you only know your present source data needs and therefore have to predict the rest. Also, purchasing and configuring hardware can be cost prohibitive.

Machine Learning algorithms powered by intelligent applications serve useful functions in our daily lives in ways we may not even be aware of. For instance, predictive analytics allow businesses to retain key customers, help assembly lines and buildings to run more efficiently, and help us find movies that we are likely to find intriguing. The ML field has gained tremendous traction and respect over the last decade, prompting Harvard Business Review to name the Data Scientist the sexiest job of the 21st century. To encourage new ML applications and foster a vibrant online community, we are thrilled to launch Cortana Intelligence Competitions, a gamification feature of Cortana Intelligence Suite, as well as our first competition Decoding Brain Signals. This platform provides an intuitive and fun environment to hone users' data science and analytics expertise, and our first competition will allow you to have the chance to contribute to the important field of neuroscience to win prizes and recognition.

Machine Learning algorithms powered by intelligent applications serve useful functions in our daily lives in ways we may not even be aware of. For instance, predictive analytics allow businesses to retain key customers, help assembly lines and buildings to run more efficiently, and help us find movies that we are likely to find intriguing. The ML field has gained tremendous traction and respect over the last decade, prompting Harvard Business Review to name the Data Scientist the sexiest job of the 21st century. To encourage new ML applications and foster a vibrant online community, we thrilled to launch Cortana Intelligence Competitions, a gamification feature of Cortana Intelligence Suite, as well as our first competition Decoding Brain Signals. This platform provides an intuitive and fun environment to hone users' data science and analytics expertise, and our first competition will allow you to have the chance to contribute to the important field of neuroscience to win prizes and recognition.