This article is a continuation of my previous one in which we were dealing in brief about what Machine Learning is, what are the techniques that we have in machine learning, etc. Here, we will be working with the Workflow of machine learning. Click here to start with machine learning on Microsoft Azure. Machine learning follows a flowchart or a Workflow with all the topics given. Question for the model This is the first very basic step that plays a major role in machine learning.
Mendes, Ana Cristina (Instituto Superior Técnico, Technical University of Lisbon and Spoken Language Systems Lab/INESC-ID Lisboa) | Coheur, Luísa (Instituto Superior Técnico, Technical University of Lisbon and Spoken Language Systems Lab/INESC-ID Lisboa)
A usual strategy to select the final answer in factoid Question-Answering (QA) relies on redundancy. A score is given to each candidate answer as a function of its frequency of occurrence, and the final answer is selected from the set of candidates sorted in decreasing order of score. For that purpose, systems often try to group together semantically equivalent answers. However, they hold several other semantic relations, such as inclusion, which are not considered, and candidates are mostly seen independently, as competitors. Our hypothesis is that not just equivalence, but other relations between candidate answers have impact on the performance of a redundancy-based QA system. In this paper, we describe experimental studies to back up this hypothesis. Our findings show that, with relatively simple techniques to recognize relations, systems' accuracy can be improved for answers of categories Number, Date and Entity.
Communication in the digital age has plenty of strange trappings. Periods can seem aggressive; complete, punctuated sentences can read as uptight. And then there's the strange space that's seemed to pop up, more and more, between the last word of a sentence and its end mark. You probably know someone who texts, chats, or tweets this way. As well as being a fully harmless phenomenon (a rarity on the internet), it's a habit that's easy to fall into when the people around you are doing it.
If you're looking for an excuse to take a small digital sabbatical, science is here to offer you a pretty good one: Your devices may be changing the way you think, according to a new study. Researchers from Dartmouth College found that individuals who use devices like laptops, tablets and smartphones for reading purposes may focus more on concrete details rather than the bigger picture. The findings suggest excessive tech use may be influencing abstract thinking. The study authors included more than 300 participants from ages 20 to 24, with an aim to measure how they processed and retained information they read based on the medium they used. To make it as fair as possible, the researchers published reading material in the same font size and format for both digital and print platforms.