Modeling anchor text and classifying queries to enhance web document retrieval

Fujii, Atsushi

doi:10.1145/1367497.1367544

Cited by 39 publications

(40 citation statements)

References 17 publications

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“…Language models generated from document titles also can be used as an approximation of a user query language model [30]. Anchor text has been widely used in the IR field to improve search effectiveness [22,23,25,31,35,36,38]. In summary, anchor texts are related to real queries, and target documents' titles.…”

Section: Query Setmentioning

confidence: 99%

Quantifying retrieval bias in Web archive search

et al. 2017

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A Web archive usually contains multiple versions of documents crawled from the Web at different points in time. One possible way for users to access a Web archive is through full-text search systems. However, previous studies have shown that these systems can induce a bias, known as the retrievability bias, on the accessibility of documents in community-collected collections (such as TREC collections). This bias can be measured by analyzing the distribution of the retrievability scores for each document in a collection, quantifying the likelihood of a document's retrieval. We investigate the suitability of retrievability scores in retrieval systems that consider every version of a document in a Web archive as an independent document. We show that the retrievability of documents can vary for different versions of the same document and that retrieval systems induce biases to different extents. We quantify this bias for a retrieval system which is adapted to handle multiple versions of the same document. The retrieval system indexes each version of a document independently, and we refine the search results using two techniques to aggregate similar versions. The first approach is to collapse similar versions of a document based on content similarity. The second approach is to collapse all versions of the same document based on their URLs. In both cases, we found that the degree of bias Universiteit Utrecht, Utrecht, The Netherlands is related to the aggregation level of versions of the same document. Finally, we study the effect of bias across time using the retrievability measure. Specifically, we investigate whether the number of documents crawled in a particular year correlates with the number of documents in the search results from that year. Assuming queries are not inherently temporal in nature, the analysis is based on the timestamps of documents in the search results returned using the retrieval model for all queries. The results show a relation between the number of documents per year and the number of documents retrieved by the retrieval system from that year. We further investigated the relation between the queries' timestamps and the documents' timestamps. First, we split the queries into different time frames using a 1-year granularity. Then, we issued the queries against the retrieval system. The results show that temporal queries indeed retrieve more documents from the assumed time frame. Thus, the documents from the same time frame were preferred by the retrieval system over documents from other time frames.

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Section: Query Setmentioning

confidence: 99%

Quantifying retrieval bias in Web archive search

et al. 2017

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“…The choice of cues was informed by previous research (Kaasten et al, 2002;Fujii, 2008;Li and Zhao, 2009;Dai and Davison, 2010;Koolen and Kamps, 2010).…”

Section: Revisiting Experimentsmentioning

confidence: 99%

MyWebSteps: Aiding Revisiting with a Visual Web History

Trien

Ruddle

2017

Interact. Comput.

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This research addresses the general topic of "keeping found things found" by investigating difficulties people encounter when revisiting webpages, and designing and evaluating a novel tool that addresses those difficulties. The research focused on occasional revisits -webpages that people have previously visited on only one day, a week or more ago (i.e., neither frequently nor recently). A three-month logging study was combined with a laboratory experiment to identify ten underlying causes of participants' revisiting failure. Overall, 61% of the failures occurred when a webpage had originally been accessed via search results, was on a topic a participant often looked at, or was on a known but large website. Then we designed a novel visual Web history tool to address the causes of failure and implemented it as a Firefox add-on. The tool was evaluated in a three-month field study, helped participants succeed on 96% of revisits, and was also used by some participants to review and reminisce about their "travels" online. Revised versions of the tool have been publicly released as the Firefox add-on MyWebSteps. RESEARCH HIGHLIGHTS• Identifying the underlying causes of failure when people revisit webpages.• Providing evidence how MyWebSteps helped users overcome those causes.

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“…This is based on the observation that titles can be used as an approximation of queries [10]. Anchor texts enrich the representation of a Web page's content to improve Web search effectiveness [4,6,8,11,[14][15][16]18]. Kanhabua and Nejdl studied the evolution of anchor texts extracted from the edit history of Wikipedia [12].…”

Section: Related Workmentioning

confidence: 99%

“…The WikiStats dataset [20] 7 , which aggregates the request history of articles from Wikimedia projects 8 . For each page, this project provides the page title, the number of requests (on hourly basis), the language in which the page is written, and the name of the project.…”

Section: Google Trends Google Trendsmentioning

confidence: 99%

Comparing Topic Coverage in Breadth-First and Depth-First Crawls Using Anchor Texts

Samar

Traub

Ossenbruggen

et al. 2016

Research and Advanced Technology for Digital Libraries

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Abstract. Web archives preserve the fast changing Web by repeatedly crawling its content. The crawling strategy has an influence on the data that is archived. We use link anchor text of two Web crawls created with different crawling strategies in order to compare their coverage of past popular topics. One of our crawls was collected by the National Library of the Netherlands (KB ) using a depth-first strategy on manually selected websites from the .nl domain, with the goal to crawl websites as completes as possible. The second crawl was collected by the Common Crawl foundation using a breadth-first strategy on the entire Web, this strategy focuses on discovering as many links as possible. The two crawls differ in their scope of coverage, while the KB dataset covers mainly the Dutch domain, the Common Crawl dataset covers websites from the entire Web. Therefore, we used three different sources to identify topics that were popular on the Web; both at the global level (entire Web) and at the national level (.nl domain): Google Trends, WikiStats, and queries collected from users of the Dutch historic newspaper archive. The two crawls are different in terms of their size, number of included websites and domains. To allow fair comparison between the two crawls, we created sub-collections from the Common Crawl dataset based on the .nl domain and the KB seeds. Using simple exact string matching between anchor texts and popular topics from the three different sources, we found that the breadth-first crawl covered more topics than the depth-first crawl. Surprisingly, this is not limited to popular topics from the entire Web but also applies to topics that were popular in the .nl domain.

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Modeling anchor text and classifying queries to enhance web document retrieval

Cited by 39 publications

References 17 publications

Quantifying retrieval bias in Web archive search

Quantifying retrieval bias in Web archive search

MyWebSteps: Aiding Revisiting with a Visual Web History

Comparing Topic Coverage in Breadth-First and Depth-First Crawls Using Anchor Texts

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