Learning Character-level Compositionality with Visual Features

Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.Comment: Accepted to ACL 201

Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles

How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on network structure of Wikipedia, we assign ranking to all articles in 9 multilingual editions of Wikipedia and investigate general ranking structure of PageRank, CheiRank and 2DRank. In particular, we focus on articles related to persons, identify top 30 persons for each rank among different editions and analyze distinctions of their distributions over activity fields such as politics, art, science, religion, sport for each edition. We find that local heroes are dominant but also global heroes exist and create an effective network representing entanglement of cultures. The Google matrix analysis of network of cultures shows signs of the Zipf law distribution. This approach allows to examine diversity and shared characteristics of knowledge organization between cultures. The developed computational, data driven approach highlights cultural interconnections in a new perspective.Comment: Published in PLoS ONE (http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0074554). Supporting information is available on the same webpag

Stories for Change

This compendium of nearly 50 best practices showcases the notable strategies that increase access to arts and culture for older adult and immigrant populations. Newcomers and older adults (65 +) are two of the fastest growing populations -- communities across the country are grappling with a demographic makeup that is increasingly diverse and proportionally older than in the past. Arts and cultural organizations have the opportunity to reach-out, to increase resources in the community, and to engage populations that are at risk for being overlooked."Stories for Change" is a compelling collection, brimming with new ideas brought to fruition by many types of organizations including: museums, libraries, community development organizations, theaters, orchestras, dance ensembles, area agencies on aging, transportation bureaus, parks, botanic gardens, universities, and more. Organizations that hope to enhance the lives of their older and immigrant residents can find approaches portrayed in these Stories that can be adapted to meet the needs of their communities.Best practices include the well-known Alzheimer's Project of the Museum of Modern Art, which has been adapted to museums around the country, and Circle of Care, a unique ride share program that partners young people with older adults to attend free arts performances in Boulder, Colorado. Stories are located in rural, mid-size, and metropolitan settings; many can be easily implemented, and do not require a major overhaul of staffing, operations, or an organization's mission

The Daily Texan

Texas Student Medi