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- item-3 hasContent frank-van-harmelen.
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- 639 creator reiner-kraft.
- 639 creator utku-irmak.
- 639 type InProceedings.
- 639 label "A Scalable Machine Learning Approach for Semi-Structured Named Entity Recognition".
- 639 sameAs 639.
- 639 abstract "Named entity recognition studies the problem of locating and classifying parts of free text into a set of predefined categories. Although extensive research has focused on the detection of person, location and organization entities, there are many other entities of interest, including phone numbers, dates, times and currencies (to name a few examples). We refer to these types of entities as "semi-structured named entities", since they usually follow certain syntactic formats according to some conventions, although their structure is typically not well-defined. Regular expression solutions require significant amount of manual effort and supervised machine learning approaches rely on large sets of labeled training data. Therefore, these approaches do not scale when we need to support many semi-structured entity types in many languages and regions. In this paper, we study this problem and propose a novel three-level bootstrapping framework for the detection of semi-structured entities. We describe the proposed techniques for phone, date and time entities, and perform extensive evaluations on English, German, Polish, Swedish and Turkish documents. Despite the minimal input from the user, our approach can achieve 95% precision and 84% recall for phone entities, and 94% precision and 81% recall for date and time entities, on average. We also discuss implementation details and report run time performance results, which show significant improvements over regular expression based solutions.".
- 639 hasAuthorList authorList.
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- 639 keyword "Structured Data Extraction from Unstructured Text".
- 639 title "A Scalable Machine Learning Approach for Semi-Structured Named Entity Recognition".
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- 645 creator bo-pang.
- 645 creator evgeniy-gabrilovich.
- 645 creator marcus-fontoura.
- 645 creator mauricio-mediano.
- 645 creator vanja-josifovski.
- 645 creator yejin-choi.
- 645 type InProceedings.
- 645 label "Using Landing Pages for Sponsored Search Ad Selection".
- 645 sameAs 645.
- 645 abstract "We explore the use of the landing page content in sponsored search ad selection. Specifically, we compare the use of the ad’s intrinsic content to augmenting the ad with the whole, or parts, of the landing page. We explore two types of extractive summarization techniques to select useful regions from the landing pages: out-of-context and in-context methods. Out-of-context methods select salient regions from the landing page by analyzing the content alone, without taking into account the ad associated with the landing page. In-context methods use the ad context (including its title, creative, and bid phrases) to help identify regions of the landing page that should be used by the ad selection engine. In addition, we introduce a simple yet effective unsupervised algorithm to enrich the ad context to further improve the ad selection. Experimental evaluation confirms that the use of landing pages can significantly improve the quality of ad selection. We also find that our extractive summarization techniques reduce the size of landing pages substantially, while retaining or even improving the performance of ad retrieval over the method that utilize the entire landing page.".
- 645 hasAuthorList authorList.
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- 645 keyword "Selection".
- 645 keyword "contextual advertisements".
- 645 keyword "ranking for search".
- 645 title "Using Landing Pages for Sponsored Search Ad Selection".
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- item-1 hasContent yejin-choi.
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- 647 creator evan-kirshenbaum.
- 647 creator george-forman.
- 647 creator shyam-rajaram.
- 647 type InProceedings.
- 647 label "A Novel Traffic Analysis for Identifying Search Fields in the Long Tail of Web Sites".
- 647 sameAs 647.
- 647 abstract "Using a clickstream sample of 2 billion URLs from many thousand volunteer Web users, we wish to analyze typical usage of keyword searches across the Web. In order to do this, we need to be able to determine whether a given URL represents a keyword search and, if so, which field contains the query. Although it is easy to recognize `q' as the query field in `http://www.google.com/search?hl=en\&q=music', we must do this automatically for the long tail of diverse websites. This problem is the focus of this paper. Since the names, types and number of fields differ across sites, this does not conform to traditional text classification or to multi-class problem formulations. The problem also exhibits highly non-uniform importance across websites, since traffic follows a Zipf distribution. We developed a solution based on manually identifying the query fields on the most popular sites, followed by an adaptation of machine learning for the rest. It involves an interesting case-instances structure: labeling each website `case' usually involves selecting at most one of the field `instances' as positive, based on seeing sample field values. This problem structure and soft constraint---which we believe has broader applicability---can be used to greatly reduce the manual labeling effort. We employed active learning and judicious GUI presentation to efficiently train a classifier with accuracy estimated at 96%, beating several baseline alternatives.".
- 647 hasAuthorList authorList.
- 647 isPartOf proceedings.
- 647 keyword "Query log".
- 647 keyword "click trail".
- 647 keyword "traffic data mining".
- 647 title "A Novel Traffic Analysis for Identifying Search Fields in the Long Tail of Web Sites".
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- item-1 hasContent george-forman.
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