ive Multi-Document Summarization via Phrase Selection and Merging∗ Lidong Bing§ Piji Li Yi Liao Wai Lam Weiwei Guo† Rebecca J. Passonneau‡ §Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA USA Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong †Yahoo Labs, Sunnyvale, CA, USA ‡Center for Computational Learning Systems, Columbi...

In mid-2005, Yahoo! Inc. began an ambitious program to create a world-class industrial research lab focusing on how to deliver services over the web to a range of stakeholders, including advertisers, site owners, content publishers, and users. The resulting organization, Yahoo! Research , has embarked on a number of research directions. In this document, we report on these directions, with a pa...

In this paper, we investigate whether text from a Community Question Answering (QA) platform can be used to predict and describe real-world attributes. We experiment with predicting a wide range of 62 demographic attributes for neighbourhoods of London. We use the text from QA platform of Yahoo! Answers and compare our results to the ones obtained from Twitter microblogs. Outcomes show that the...

This paper describes automatic Web-page classification by using machine learning methods. Recently, the importance of portal site services is increasing including the search engine function on World Wide Web. Especially, the portal site such as for Yahoo! service which hierarchically classifies Web-pages into many categories is becoming popular. However, the classification of Web-page into each...

Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Questions are submitted on QABB and let somebody in the internet answer them. Communications on QABB connect users, and the overall connections can be regarded as a social network. If the evolution of the network can be predicted, it is quite useful for encouraging communicat...

This paper focuses on analyzing and predicting not-answered questions in Community based Question Answering (CQA) services, such as Yahoo! Answers. In CQA, users express their information needs by submitting questions and await answers from other users. One of the key problems of this pattern is that sometimes no one helps to give answers. In this paper, we analyze the not-answered questions an...

Approximately 94 million American adults use the internet on a typical day [24]. The number one internet activity is reading and writing email. Search engine use is next in line and continues to increase in popularity. In fact, survey findings indicate that nearly 60 million American adults use search engines on a given day. Even though there are many internet search engines, Google, Yahoo!, an...

Background: Geo-Net-PT is a geospatial ontology representing the Portuguese territory and the relations between the several locations within it. Yahoo! GeoPlanet is a geospatial ontology that covers the whole world. To interlink the two ontologies and reduce the effects of repeated information, we propose an automatic alignment between their administrative parts, based on name similarity and ph...

We describe PNUTS, a massively parallel and geographically distributed database system for Yahoo!’s web applications. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and util...