Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize...

We present the approach we took for our participation to the WMT12 Quality Estimation Shared Task: our main goal is to achieve reasonably good results without appeal to supervised learning. We have used various similarity measures and also an external resource (Google N -grams). Details of results clarify the interest of such an approach.

In this paper we investigate techniques for semi-supervised learning that split their unsupervised and supervised components — that is, an initial unsupervised phase is followed by a supervised learning phase. We first analyze the relative value of labeled and unlabeled data. We then present methods that perform “split” semi-supervised learning and show promising empirical results.

We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods that are increasingly being employed in fMRI data analysis. Whereas SVM associates properties of the imaging data with simple specific categorical labels, KCCA replaces these simple labels with a label vector for each stimulus containing...

This paper presents a method for mining potential troubles or obstacles related to the use of a given object. Some example instances of this relation are 〈medicine, side effect〉 and 〈amusement park, height restriction〉. Our acquisition method consists of three steps. First, we use an unsupervised method to collect training samples from Web documents. Second, a set of expressions generally refer...

We describe our method for automatic bird species classification, which uses raw audio without segmentation and without using any auxiliary metadata. It successfully classifies among 501 bird categories, and was by far the highest scoring audio-only bird recognition algorithm submitted to BirdCLEF 2014. Our method uses unsupervised feature learning, a technique which learns regularities in spec...

In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are “wrapper” techniques that require a learning algorithm to eval...

The processes that determine unsupervised categorization, the task of classifying stimuli without guidance or feedback, are poorly understood. Two experiments examined the emergence and plasticity of unsupervised strategies using perceptual stimuli that varied along two separable dimensions. In the first experiment, participants either classified stimuli into any two categories of their choice ...