1. Evidence Aggregation: In last year’s track there were two different methods in general for obtaining a visit ranking out of reports (smaller document units), i.e., (A) using reports as indexing and retrieval units and then converting a report ranking into a visit ranking, and (B) using visits as indexing and retrieval units by concatenating reports at the very first stage and then obtain a v...

We present our approach for tackling the iUnit ranking and iUnit summarization subtasks of MobileClick2. We first conduct intent discovery based on latent topic modeling. Our iUnit ranking method exploits the discovered intents and considers the importance of an iUnit in each Web content document. We further develop our iUnit summarization model using the outcome from the iUnit ranking subtask....

Today, users expect a variety of digital libraries to be searchable from a single Web page. The German Vascoda project provides this service for dozens of information sources. Its ultimate goal is to provide search quality close to the ranking of a central database containing documents from all participating libraries. Currently, however, the Vascoda portal is based on a non-cooperative metasea...

Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous collections. Previous research has shown that combining passage-level evidence with pseudo relevance feedback brings added benefits. In this pap...

The world is addicted to ranking: everything, from the reputation of scientists, journals, and universities to purchasing decisions is driven by measured or perceived differences between them. Here, we analyze empirical data capturing real time ranking in a number of systems, helping to identify the universal characteristics of ranking dynamics. We develop a continuum theory that not only predi...

In the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine the authorship of the anonymous text. Practically all approaches solved this problem with machine learning methods. The input attributes for the machine learning process are usually formed by stylistic or grammatical prope...

For Information Retrieval, users are more concerned about the precision of top ranking documents in most practical situations. In this paper, we propose a method to improve the precision of top N ranking documents by reordering the retrieved documents from the initial retrieval. To reorder documents, we first automatically extract Global Key Terms from document set, then use extracted Global Ke...

AbstrAct: The textual content of company annual reports has proven to contain predictive indicators for the company fu­ ture performance. This paper addresses the general re search question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive mod­ els with annual reports. More specifically, we focus on these two questions: 1) the feasibi...

Feature Extraction is a mechanism used to extract key phrases from any given text documents. This extraction can be weighted, ranked or semantic based. Weighted and Ranking based feature extraction normally assigns scores to extracted words based on various heuristics. Highest scoring words are seen as important. Semantic based extractions normally try to understand word meanings, and words wit...