In this paper we present Carnegie Mellon University’s submission to the TREC 2009 Relevance Feedback Track. In this submission we take a classification approach on document pairs to using relevance feedback information. We explore using textual and non-textual document-pair features to classify unjudged documents as relevant or non-relevant, and use this prediction to re-rank a baseline documen...

Relying on the idea that back-of-the-book indexes are traditional devices for navigation through large documents, we have developed a method to build a hypertextual network that helps the navigation in a document. Building such an hypertextual network requires selecting a list of descriptors, identifying the relevant text segments to associate with each descriptor and finally ranking the descri...

This paper describes our approaches and results in NTCIR10 INTENT-2 task. In this year, we participate in subtasks for both the Chinese and English topics. We extract subtopics from multiple resources for these topics, and several subtopic clustering and re-ranking methods are proposed in this work. In Document Ranking subtask, we redefine the novelty of a document and use the new definition to...

For NTCIR Workshop 7 UC Berkeley participated in IR4QA (Information Retrieval for Question Answering) as well as the Patent Mining tracks. For IR4QA we only did Japanese monolingual search. Our focus was thus upon Japanese topic search against the Japanese News document collection as in past NTCIR participations. We preprocessed the text using the ChaSen morphological analyzer for term segmenta...

We demonstrate effective new methods of document ranking based on lexical cohesive relationships between query terms. The proposed methods rely solely on the lexical relationships between original query terms, and do not involve query expansion or relevance feedback. Two types of lexical cohesive relationship information between query terms are used in document ranking: short-distance collocati...

Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, ...

The currently available document retrieval tools are usually still based on simple keyword search methods and according result list or a static (hierarchical) classification of the considered document collection. Both approaches neither allow a user to adapt the ranking or the classification to his needs, nor provide visualization methods to support the user in browsing or analysing a document ...

Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document,...