In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.

RDF/XML has been widely recognized as the standard for annotating online Web documents and for transforming the HTML Web to the so called Semantic Web. In order to enable widespread usability for the Semantic Web there is a need to bootstrap large, rich and up-todate domain ontologies that organize most relevant concepts, their relationships and instances. In this paper, we present automated techiques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant Web sites provided by the user. We develop algorithms that detect and utilize HTML regularities in the Web documents to turn them into hierarchical semantic structures encoded as XML. Next, we present tree-mining algorithms that identify key domain concepts and their taxonomical relationships. We also extract semistructed concept instances annotated with their labels whenever they are available. Experimental evaluation for the News and Hotels domain indicates that our algorithms can bootstrap and populate domain specific ontologies with high precision and recall.

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with taskspecific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between 1 -regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.

World Wide Web is transforming itself into the largest information resource making the process of information extraction (IE) from Web an important and challenging problem. In this paper, we present an automated IE system that is domain independent and that can automatically transform a given Web page into a semi-structured hierarchical document using presentation regularities. The resulting documents are weakly annotated in the sense that they might contain many incorrect annotations and missing labels. We also describe how to improve the quality of weakly annotated data by using domain knowledge in terms of a statistical domain model. We demonstrate that such system can recover from ambiguities in the presentation and boost the overall accuracy of a base information extractor by up to 20%. Our experimental evaluations with TAP data, computer science department Web sites, and RoadRunner document sets indicate that our algorithms can scale up to very large data sets.

In this paper, we present a system for clustering the search results of a news search engine. The news search interface includes the relevant news articles to a given query organized in terms of related news stories. Here each cluster corresponds to a news story and the news articles are clustered into stories. We present a system that clusters the search results of a news search system in a fast and scalable manner. The clustering system is organized into three components including offline clustering, incremental clustering and realtime clustering. We propose novel techniques for clustering the search results in realtime. The experimental results with large collections of news documents reveal that our system is both scalable and also achieves good accuracy in clustering the news search results.

In this paper we describe the semantic partitioner algorithm, that uses the structural and presentation regularities of the Web pages to automatically transform them into hierarchical content structures. These content structures enable us to automatically annotate labels in the Web pages with their semantic roles, thus yielding meta-data and instance information for the Web pages. Experimental results with the TAP knowledge base and computer science department Web sites, comprising 16, 861 Web pages indicate that our algorithm is able gather meta-data accurately from various types of Web pages. The algorithm is able to achieve this performance without any domain specific engineering requirement.

Abstract

Entity ranking is a recent paradigm that refers to retrieving and ranking related objects and entities from different structured sources in various scenarios. Entities typically have associated categories and relationships with other entities. In this work, we present an extensive analysis of Web-scale entity ranking, based on machine learned ranking models using an ensemble of pairwise preference models. Our proposed system for entity ranking uses structured knowledge bases, entity relationship graphs and user data to derive useful features to facilitate semantic search with entities directly within the learning to rank framework. The experimental results are validated on a large-scale graph containing millions of entities and hundreds of millions of entity relationships. We show that our proposed ranking solution clearly improves a simple user behavior based ranking model.