Matches in ScholarlyData for { ?s ?p ?o. }
- item-3 hasContent yunzhang-zhu.
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- item-4 hasContent hua-li.
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- item-5 hasContent zheng-chen.
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- 107 type InProceedings.
- 107 label "Predicting Click Through Rate for Job Listings".
- 107 sameAs 107.
- 107 abstract "Click Through Rate (CTR) is an important metric for ad systems, job portals, recommendation systems. CTR impacts publisher's revenue, advertiser's bid amounts in "pay for performance" business models. We learn regression models using features of the job, optional click history of job, features of "related" jobs. We show that our models predict CTR much better than predicting avg. CTR for all job listings, even in absence of the click history for the job listing.".
- 107 hasAuthorList authorList.
- 107 isPartOf proceedings.
- 107 keyword "Poster Session".
- 107 title "Predicting Click Through Rate for Job Listings".
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- item-1 type ListItem.
- item-1 hasContent manish-gupta.
- 108 type InProceedings.
- 108 label "Query Clustering using Click-Through Graph".
- 108 sameAs 108.
- 108 abstract "In this p aper w e describe a problem of d iscovering query clusters from a click -through graph of w eb search logs. The graph consists of a set of web search queries, a set of pag es selected for the queries, and a set of d irected edges that connects a query node and a page node click ed by a user for the query. The proposed method extracts all m axim al b ipartite cliques (b icliques) from a click-through graph and compute an equiv alence set of queries (i.e., a query cluster) from the m axim al bicliques. A cluster of queries is form ed from th e queries in a biclique. We present a scalable algorithm that enumerates all maximal bicliques from the click-through graph. We h ave conducted experim ents on Yahoo web search queries and the result is p romising.".
- 108 hasAuthorList authorList.
- 108 isPartOf proceedings.
- 108 keyword "Poster Session".
- 108 title "Query Clustering using Click-Through Graph".
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- item-1 hasContent jeonghee-yi.
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- item-2 type ListItem.
- item-2 hasContent farzin-maghoul.
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- 109 type InProceedings.
- 109 label "An Effective Semantic Search Technique using Ontology".
- 109 sameAs 109.
- 109 abstract "In this paper, we present a semantic search technique considering the type of desired Web resources and the semantic relationships between the resources and the query keywords in the ontology. In order to effectively retrieve the most relevant top-k resources, we propose a novel ranking model. To do this, we devise a measure to determine the weight of the semantic relationship. In addition, we consider the number of meaningful semantic relationships between a resource and keywords, the coverage of keywords, and the distin- guishability of keywords. Through experiments using real datasets, we observe that our ranking model provides more accurate seman- tic search results compared to existing ranking models.".
- 109 hasAuthorList authorList.
- 109 isPartOf proceedings.
- 109 keyword "Poster Session".
- 109 title "An Effective Semantic Search Technique using Ontology".
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- authorList hasItem item-3.
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- item-1 type ListItem.
- item-1 hasContent jihyun-lee.
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- item-2 type ListItem.
- item-2 hasContent jun-ki-min.
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- item-3 type ListItem.
- item-3 hasContent chin-wan-chung.
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- 11 type InProceedings.
- 11 label "StatSnowball: a Statistical Approach to Extracting Entity Relationships".
- 11 sameAs 11.
- 11 abstract "Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Boot- strapping systems significantly reduce the number of train- ing examples, but they usually apply heuristic-based meth- ods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Further- more, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify var- ious types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a boot- strapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic net- works (MLNs) and softens hard rules by learning their weights in a maximum likelihood estimate sense. MLN is a general model, and can be configured to perform different levels of relation extraction. In StatSnwoball, pattern selection is performed by solving an l1 -norm penalized maximum like- lihood estimation, which enjoys well-founded theories and efficient solvers. We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data. Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during it- erations with a small number of seeds, and the joint inference of MLN can improve the performance. Finally, StatSnowball is efficient and we have developed a working entity relation search engine called Renlifang based on it.".
- 11 hasAuthorList authorList.
- 11 isPartOf proceedings.
- 11 keyword "Data Mining".
- 11 title "StatSnowball: a Statistical Approach to Extracting Entity Relationships".
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- item-1 type ListItem.
- item-1 hasContent jun-zhu.
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- item-2 type ListItem.
- item-2 hasContent zaiqing-nie.
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- item-3 hasContent xiaojiang-liu.
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- item-4 hasContent bo-zhang.
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- item-5 hasContent ji-rong-wen.