Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Extracting semantic relations from query logs

Abstract: In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.

Search systems and methods using in-line contextual queries

Abstract: Systems and methods are provided for implementing searches using contextual information associated with a Web page (or other document) that a user is viewing when a query is entered. The page includes a contextual search interface that has an associated context vector representing content of the page. When the user submits a search query via the contextual search interface, the query and the context vector are both provided to the query processor and used in responding to the query.

Graph Clustering and Minimum Cut Trees

Abstract: In this paper, we introduce simple graph clustering methods based on minimum cuts within the graph. The clustering methods are general enough to apply to any kind of graph but are well suited for graphs where the link structure implies a notion of reference, similarity, or endorsement, such as web and citation graphs. We show that the quality of the produced clusters is bounded by strong minimum cut and expansion criteria. We also develop a framework for hierarchical clustering and present applications to real-world data. We conclude that the clustering algorithms satisfy strong theoretical criteria and perform well in practice.

Spectral Regression for Efficient Regularized Subspace Learning

Abstract: Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.

Carboplatin- or Cisplatin-Based Chemotherapy in First-Line Treatment of Small-Cell Lung Cancer: The COCIS Meta-Analysis of Individual Patient Data

Abstract: Purpose Since treatment efficacy of cisplatin- or carboplatin-based chemotherapy in the first-line treatment of small-cell lung cancer (SCLC) remains contentious, a meta-analysis of individual patient data was performed to compare the two treatments. Patients and Methods A systematic review identified randomized trials comparing cisplatin with carboplatin in the first-line treatment of SCLC. Individual patient data were obtained from coordinating centers of all eligible trials. The primary end point was overall survival (OS). All statistical analyses were stratified by trial. Secondary end points were progression-free survival (PFS), objective response rate (ORR), and treatment toxicity. OS and PFS curves were compared by using the log-rank test. ORR was compared by using the Mantel-Haenszel test. Results Four eligible trials with 663 patients (328 assigned to cisplatin and 335 to carboplatin) were included in the analysis. Median OS was 9.6 months for cisplatin and 9.4 months for carboplatin (hazard rati...