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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Proceedings ArticleDOI
18 Jul 2019
TL;DR: This article proposed a joint approach that incorporates BERT's classification vector into existing neural models and showed that it outperforms state-of-the-art ad-hoc ranking baselines.
Abstract: Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several ex-sting neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.

257 citations

Proceedings ArticleDOI
01 Aug 1998
TL;DR: There was only a slight difference in performance between the original English queries and the best crosslanguage queries, i.e., structured queries with medical dictionary and general dictionary translation.
Abstract: In this study, the effects of query structure and various setups of translation dictionaries on the performance of cross-language information retrieval (CLIR) were tested. The document collection was a subset of the TREC collection, and as test requests the study used TREC's health related topics. The test system was the INQUERY retrieval system. The performance of translated Finnish queries against English documents was compared to the performance of original English queries against English documents. Four natural language query types and three query translation methods, using a general dictionary and a domain specific (= medical) dictionary, were studied. There was only a slight difference in performance between the original English queries and the best crosslanguage queries, i.e., structured queries with medical dictionary and general dictionary translation. The structuring of queries was done on the basis of the output of dictionaries.

255 citations

Posted Content
TL;DR: It is found that in the author co-citation network, citation rank is highly correlated with PageRank's with different damping factors and also with different PageRank algorithms; citation rank and PageRank are not significantly correlation with centrality measures; and h-index is not significantly correlated withcentrality measures.
Abstract: Google's PageRank has created a new synergy to information retrieval for a better ranking of Web pages. It ranks documents depending on the topology of the graphs and the weights of the nodes. PageRank has significantly advanced the field of information retrieval and keeps Google ahead of competitors in the search engine market. It has been deployed in bibliometrics to evaluate research impact, yet few of these studies focus on the important impact of the damping factor (d) for ranking purposes. This paper studies how varied damping factors in the PageRank algorithm can provide additional insight into the ranking of authors in an author co-citation network. Furthermore, we propose weighted PageRank algorithms. We select 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculate the ranks of these 108 authors based on PageRank with damping factor ranging from 0.05 to 0.95. In order to test the relationship between these different measures, we compare PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank's with different damping factors and also with different PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index is not significantly correlated with centrality measures.

255 citations

Patent
25 Apr 2002
TL;DR: In this article, a spoken query is represented as a lattice indicating possible sequential combinations of words in the spoken query, and the lattice is converted to a query certainty vector.
Abstract: A system and method indexes and retrieves documents stored in a database. A document feature vector is extracted for each document to be indexed. The feature vector is projected to a low dimension document feature vector, and the documents are indexed according to the low dimension document feature vectors. A spoken query is represented as a lattice indicating possible sequential combinations of words in the spoken query. The lattice is converted to a query certainty vector, which is also projected to a low dimension query certainty vector. The low dimension query vector is compared to each of the low dimension document feature vectors to retrieve a matching result set of documents.

255 citations

Patent
05 Aug 2010
TL;DR: In this paper, a facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows: after receiving the visual query with one or multiple facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria.
Abstract: A facial recognition search system identifies one or more likely names (or other personal identifiers) corresponding to the facial image(s) in a query as follows. After receiving the visual query with one or more facial images, the system identifies images that potentially match the respective facial image in accordance with visual similarity criteria. Then one or more persons associated with the potential images are identified. For each identified person, person-specific data comprising metrics of social connectivity to the requester are retrieved from a plurality of applications such as communications applications, social networking applications, calendar applications, and collaborative applications. An ordered list of persons is then generated by ranking the identified persons in accordance with at least metrics of visual similarity between the respective facial image and the potential image matches and with the social connection metrics. Finally, at least one person identifier from the list is sent to the requester.

254 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168