Number of items: 3.
Sire, Stéphane and
Paquier, Micaël and
Vagner, Alain and
Bogaerts, Jérôme A Messaging API for Inter-Widgets Communication. Widget containers are used everywhere on the Web, for instance as customizable start pages to Web desktops. In this poster, we describe the extension of a widget container with an inter-widgets communication layer, as well as the subsequent application programming interfaces (APIs) added to the Widget object to support this feature. We present the benefits of a drag and drop facility within widgets and conclude by a call for standardization of inter-widgets communication on the Web.
Baykan, Eda and
Henzinger, Monika and
Marian, Ludmila and
Weber, Ingmar Purely URL-based Topic Classification. Given only the URL of a web page, can we identify its topic? This is the question that we examine in this paper. Usually, web pages are classiï¬ed using their content [7], but a URL-only classiï¬er is preferable, (i) when speed is crucial, (ii) to enable content ï¬ltering before an (objectionable) web page is downloaded, (iii) when a page’s content is hidden in images, (iv) to annotate hyperlinks in a personalized web browser, without fetching the target page, and (v) when a focused crawler wants to infer the topic of a target page before devoting bandwidth to download it. We apply a machine learning approach to the topic identiï¬cation task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classiï¬ers for each topic, we achieve typical F-measure values between 80 and 85, and a typical precision of around 85. We also ran experiments on a small data set of university web pages. For the task of classifying these pages into faculty, student, course and project pages, our methods improve over previous approaches by 13.8 points of F-measure.
Michel, Sebastian and
Weber, Ingmar Rethinking Email Message and People Search. We show how a number of novel email search features can be implemented without any kind of natural language processing (NLP) or advanced data mining. Our approach inspects the email headers of all messages a user has ever sent or received and it creates simple per-contact summaries, including simple information about the message exchange history, the domain of the sender or even the sender’s gender. With these summaries advanced questions/tasks such as “Who do I still need to reply to?†or “Find ‘fun’ messages sent by friends.†become possible. As a proof of concept, we implemented a Mozilla-Thunderbird extension, adding powerful people search to the popular email client.
This list was generated on Fri Feb 15 09:01:07 2019 GMT.
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