This paper examines the relationship between human generated tags describing different parts of the same song. These tags were collected using Amazon’s Mechanical Turk service. We find that the agreement between different people’s tags decreases as the distance between the parts of a song that they heard increases. To model these tags and these relationships, we describe a conditional restricte...

Folksonomies are Web 2.0 platforms where users share resources with each other. Furthermore, they can assign keywords (called tags) to the resources for categorizing and organizing the resources. Numerous types of resources like websites (Delicious), images (Flickr), and videos (YouTube) are supported by different folksonomies. The folksonomies are easy to use and thus attract the attention of ...

We describe work on large scale automatic annotation of full texts of books with social tags. Our task consisted of assigning tags to the full texts of works of fiction and evaluating them against tags assigned by humans. We compared Boosting and Relevance Models (RM) methods to explore how they differ primarily in terms scalability and also annotation quality. We extended beyond the set of 50 ...

We present a scalable joint language model designed to utilize fine-grain syntactic tags. We discuss challenges such a design faces and describe our solutions that scale well to large tagsets and corpora. We advocate the use of relatively simple tags that do not require deep linguistic knowledge of the language but provide more structural information than POS tags and can be derived from automa...

Users share their images in a Folksonomy (like Flickr) and add tags (keywords) to these images. For example an image of “Deutsches Eck” can be tagged with the tags Koblenz, Germany, and Deutsches Eck. Tags can be used as useful features for learning a classifier. In addition to tags, we can also exploit low level image features for image classification. In this paper, we show that merging tags ...

Social tags are an important component of “Web2.0” music recommendation websites. In this paper we propose a method for predicting social tags using audio features and supervised learning. These automatically-generated tags (or “autotags”) can furnish information about music that is untagged or poorly tagged. The tags can also serve to smooth the tag space from which similarities and recommenda...

We have analyzed the pattern of gene expression in human primary CD34(+) stem/progenitor cells. We identified 42,399 unique serial analysis of gene expression (SAGE) tags among 106,021 SAGE tags collected from 2.5 x 10(6) CD34(+) cells purified from bone marrow. Of these unique SAGE tags, 21,546 matched known expressed sequences, including 3,687 known genes, and 20,854 were novel without a matc...

Many today’s online communities use TagClouds, an aesthetic and easy to understand visualization, to represent popular tags collaboratively generated by their users. However, due to the free nature of tagging, such collaborative tags have some linguistic problems and certain other intrinsic limitations, such as high semantic density. Moreover, the alphabetical order of TagClouds poorly supports...

In RFID system, one of the problems that we must solve is the collision between tags that may occur when a reader attempts to read a number of tags simultaneously. Most of the popular anti-collision algorithms are based on the ALOHA-type algorithm, which are simple and show good performance when the number of tags to read is small. However, for ALOHA-type algorithms the time required to read th...

A mobile RFID system is a radio frequency identification technology that allows users to read the information on its tags. When this free reading function is combined with a mobile RFID system featuring radio frequency identification, it may violate an individuals privacy. This is because others may obtain personal information by reading the tags. In addition, user tracking can be a problem, be...