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- 132 type InProceedings.
- 132 label "Modeling Semantics and Structure of Discussion Threads".
- 132 sameAs 132.
- 132 abstract "The abundant knowledge in web communities has motivated the research interests in discussion threads. The dynamic nature of discussion threads poses interesting and challenging problems for computer scientists. Although techniques such as semantic models or structural models have been shown to be useful in a number of areas, they are inefficient in understanding discussion threads due to the temporal dependence among posts in a discussion thread. Such dependence causes that semantics and structure coupled with each other in discussion threads. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantic and Structure of discussion threads.".
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- 132 keyword "Poster Session".
- 132 title "Modeling Semantics and Structure of Discussion Threads".
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- item-1 hasContent chen-lin.
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- item-2 hasContent jiang-ming-yang.
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- item-3 hasContent rui-cai.
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- 133 type InProceedings.
- 133 label "Combining Anchor Text Categorization and Graph Analysis for Paid Link Detection".
- 133 sameAs 133.
- 133 abstract "In order to artificially boost the rank of commercial pages in search engine results, search engine optimizers pay for links to these pages on other websites. Identifying paid links is important for a web search engine to produce highly relevant results. In this paper we introduce a novel method of identifying such links. We start with training a classifier of anchor text topics and analyzing web pages for diversity of their outgoing commercial links. Then we use this information and analyze link graph of the Russian Web to find pages that sell links and sites that buy links and to identify the paid links. Testing on manually marked samples showed high efficiency of the algorithm.".
- 133 hasAuthorList authorList.
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- 133 keyword "Poster Session".
- 133 title "Combining Anchor Text Categorization and Graph Analysis for Paid Link Detection".
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- item-1 hasContent kirill-nikolaev.
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- item-2 hasContent ekaterina-zudina.
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- item-3 hasContent andrey-gorshkov.
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- 134 type InProceedings.
- 134 label "Rethinking Email Message and People Search".
- 134 sameAs 134.
- 134 abstract "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.".
- 134 hasAuthorList authorList.
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- 134 keyword "Poster Session".
- 134 title "Rethinking Email Message and People Search".
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- item-1 hasContent sebastian-michel.
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- item-2 hasContent ingmar-weber.
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- 135 type InProceedings.
- 135 label "Purely URL-based Topic Classification".
- 135 sameAs 135.
- 135 abstract "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 classified using their content [7], but a URL-only classifier is preferable, (i) when speed is crucial, (ii) to enable content filtering 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 identification task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classifiers 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.".
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