Jazz legends like William James "Count" Baise and Duke Ellington became regulars at some of Boston’s top venues between the 1950s and 1960s. Jazz flourished, particularly in the South End neighborhood, where clubs like Hi-Hat, Savoy Cafe and Wally’s Cafe drew in crowds and talent. Iconic venues like Symphony Hall, built in 1900, set the stage for classical music performances, hosting renowned orchestras and conductors. While 19th century Boston saw Celtic music thriving due to the waves of Irish immigration, the 20th century was quite the mixed bag, from prestigious orchestras to being a major hub in the global punk rock scene.īoston’s vibrant music scene was fueled by its prestigious academic institutions and cultural heritage. It’s no surprise that one of Boston’s nicknames is actually The Hub of the Universe!īoston Concerts and Music Scene Through the Ages These events attract music lovers from all over the world and showcase a diverse range of musical genres. Boston is home to several music festivals throughout the year, such as the undisputed king of annual music events, Boston Calling Music Festival and the Boston Celtic Music Festival. Experiments on three real-world datasets show that the proposed TextTruth model can accurately select trustworthy answers, even when these answers are formed by multiple factors.Boston, the commonwealth of Massachusetts, is renowned for its rich cultural heritage and thriving music scene, boasting a wide range of concert venues that cater to every musical taste and genre.Īs a dominant sports town and a huge hub in the East Coast, you'll always find great things to do in Boston - and it’s also a thriving music location in its own right. The proposed method works in an unsupervised manner, and thus can be applied to various application scenarios that involve text data. After that, the answers to each question can be ranked based on the estimated trustworthiness of factors. To tackle these challenges, in this paper, we propose a novel truth discovery method, named “TextTruth”, which jointly groups the keywords extracted from the answers of a specific question into multiple interpretable factors, and infers the trustworthiness of both answer factors and answer providers. The major challenges of inferring true information on text data stem from the multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). However, most existing truth discovery methods are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data as text data has its unique characteristics. Truth discovery has attracted increasingly more attention due to its ability to distill trustworthy information from noisy multi-sourced data without any supervision. TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data Hengtong Zhang (SUNY at Buffalo) Yaliang Li (Baidu Research) Fenglong Ma (SUNY Buffalo) Jing Gao (University at Buffalo) Lu Su (The State University of New York at Buffalo)
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