A new link-based document ranking framework is devised with at its heart, a contents and time sensitive random literature explorer designed to more accurately model the behaviour of readers of scientific documents. In particular, our ranking framework dynamically adjusts its random walk parameters according to both contents and age of encountered documents, thus incorporating the diversity of t...

The usefulness of a translated text for gisting purposes strongly depends on the overall translation quality of the text, but especially on the translation quality of the most informative portions of the text. In this paper we address the problems of ranking translated sentences within a document and ranking translated documents within a set of documents on the same topic according to their inf...

This paper describes our approaches and results in NTCIR11 IMine task. In 2014, we participate in subtasks for Chinese/English Subtopic Mining and Chinese Document Ranking. In Subtopic Mining subtask, we mine subtopic candidates from various resources and construct the subtopic hierarchy with several different strategies. In Document Ranking subtask, we rerank the result lists with HITS algorit...

Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs)....

This paper describes our work at the sixth NTCIR workshop on the subtask of C-C single language information retrieval. We compared label propagation (LP), K-nearest neighboring (KNN), and relevance feedback (RF) for document re-ranking and found that RF is a more robust technique for performance improvement, while LP and KNN are sensitive to the choice and the number of relevant documents for s...

Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS) fo...

In this paper, we describe our participation in the English Subtopic Mining and Document Ranking subtasks of the NTCIR-11 IMINE Task. In the Subtopic Mining subtask, we mine subtopics from query suggestions, query dimensions, and Freebase entities of a given query, rank them based on their importance for the given query, and finally construct a two-level hierarchy of subtopics. In the Document ...

In this paper, we propose a preference framework for information retrieval in which the user and the system administrator are enabled to express preference annotations on search keywords and document elements, respectively. Our framework is flexible and allows expressing preferences such as “A is infinitely more preferred than B,” which we capture by using hyperreal numbers. Due to the widespre...

The goal of the d-Rank project is to study rank aggregation in scientific publication databases. In our work we focus in particular on document ranking in the domain of particle physics and we work with the collection of CERN publications called the CERN Document Server. In this report we present the main advances achieved within the second phase of the project. The most important achievements ...

Document enrichment is the task of retrieving additional knowledge from external resource over what is available through source document. This task is essential because of the phenomenon that text is generally replete with gaps and ellipses since authors assume a certain amount of background knowledge. The recovery of these gaps is intuitively useful for better understanding of document. Conven...