Parsing the semantic structure of a web page is a key component of web information extraction. Successful extraction algorithms usually require large-scale training and evaluation datasets, which are difficult to acquire. Recently, crowdsourcing has proven to be an effective method of collecting large-scale training data in domains that do not require much domain knowledge. For more complex domains, researchers have proposed sophisticated quality control mechanisms to replicate tasks in parallel or sequential ways and then aggregate responses from multiple workers. Conventional annotation integration methods often put more trust in the workers with high historical performance; thus, they are called performance-based methods. Recently, Rzeszotarski and Kittur have demonstrated that behavioral features are also highly correlated with annotation quality in several crowdsourcing applications. In this article, we present a new crowdsourcing system, called Wernicke, to provide annotations for web information extraction. Wernicke collects a wide set of behavioral features and, based on these features, predicts annotation quality for a challenging task domain: annotating web page structure. We evaluate the effectiveness of quality control using behavioral features through a case study where 32 workers annotate 200 Q&A web pages from five popular websites. In doing so, we discover several things: (1) Many behavioral features are significant predictors for crowdsourcing quality. (2) The behavioral-feature-based method outperforms performance-based methods in recall prediction, while performing equally with precision prediction. In addition, using behavioral features is less vulnerable to the cold-start problem, and the corresponding prediction model is more generalizable for predicting recall than precision for cross-website quality analysis. (3) One can effectively combine workers’ behavioral information and historical performance information to further reduce prediction errors.

In this paper we discuss our research activities in the area of spoken lecture processing. Our goal is to improve the access to on-line audio/visual recordings of academic lectures by developing tools for the processing, transcription, indexing, segmentation, summarization, retrieval and browsing of this media. In this paper, we provide an overview of the technology components and systems that have been developed as part of this project, present some experimental results, and discuss our ongoing and future research plans. Index Terms:spoken lecture processing, spoken document retrieval, audio browsing

The early Web was hailed for giving individuals the same publishing power as large content providers. But over time, large content providers learned to exploit the structure in their data, leveraging databases and server side technologies to provide rich browsing and visualization. Individual authors fall behind once more: neither old-fashioned static pages nor domain-specific publishing frameworks supporting limited customization can match custom database-backed web applications.

Existing augmentations of web pages are mostly small cosmetic changes (e.g., removing ads) and minor addition of third-party content (e.g., product prices from competing sites). None leverages the structured data presented in web pages. This paper describes Sifter, a web browser extension that can augment a well-structured web site with advanced filtering and sorting functionality. These added features work inside the site's own pages, preserving the site's presentational style and the user's context. Sifter contains an algorithm that scrapes structured data out of well-structured web pages while usually requiring no user intervention. We tested Sifter on real web sites and real users and found that people could use Sifter to perform sophisticated queries and high-level analyses on sizable data collections on the Web. We propose that web sites can be similarly augmented with other sophisticated data-centric functionality, giving users new benefits over the existing Web.

No abstract available.

Dialog boxes that collect parameters for commands often create ephemeral, unnatural interruptions of a program's normal execution flow, encouraging the user to complete the dialog box as quickly as possible in order for the program to process that command. In this paper we examine the idea of turning the act of collecting parameters from a user into a first class object called a user interface continuation. Programs can create user interface continuations by specifying what information is to be collected from the user and supplying a callback (i.e., a continuation) to be notified with the collected information. A partially completed user interface continuation can be saved as a new command, much as currying and partially evaluating a function with a set of parameters produces a new function. Furthermore, user interface continuations, like other continuation-passing paradigms, can be used to allow program execution to continue uninterrupted while the user determines a command's parameters at his or her leisure.