The number of online documents has rapidly grown, and with the expansion of the Web,
document analysis, or text analysis, has become an essential task for preparing, storing …

We illustrate the design and use of open-ended survey questions to elicit people's first-order
concerns on policies. Closed-ended questions are the backbone of surveys but may prime …

Short text clustering has become an increasingly important task with the popularity of social
media like Twitter, Google+, and Facebook. It is a challenging problem due to its sparse …

Organizing data into sensible groupings is one of the most fundamental modes of
understanding and learning. As an example, a common scheme of scientific classification …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

We designed and implemented TAGME, a system that is able to efficiently and judiciously
augment a plain-text with pertinent hyperlinks to Wikipedia pages. The specialty of TAGME …

In microblogging services such as Twitter, the users may become overwhelmed by the raw
data. One solution to this problem is the classification of short text messages. As short texts …

Short text clustering is a challenging problem due to its sparseness of text representation.
Here we propose a flexible Self-Taught Convolutional neural network framework for Short …

This paper describes how to automatically cross-reference documents with Wikipedia: the
largest knowledge base ever known. It explains how machine learning can be used to …

This paper presents a general framework for building classifiers that deal with short and
sparse text & Web segments by making the most of hidden topics discovered from large …