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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.


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Proceedings ArticleDOI
01 Aug 1998
TL;DR: This paper implemented the model and ran a series of experiments to show that, in addition to the added functionality, the use of the structural information embedded in SGML documents can improve the effectiveness of document retrieval, compared to the case where no such information is used.
Abstract: In traditional information retrieval (IR) systems, a document as a whole is the target for a query. With increasing interests in structured documents like SGML documents, there is a growing need to build an LR system that can retrieve parts of documents, which satisfy not only content-based but also structure-based requirements. In this paper, we describe an inference-net-based approach to this problem. The model is capable of retrieving elements at any level in a principled way, satisfying certain containment constraints in a quety. Moreover, lvhile the model is general enough to reproduce the ranking strategy adopted by conventional document retrieval systems by making use of document and collection level statistics such as TF and IDF, its flexibility allows for incorporation of a variety of pragmatic and semantic information associated with document structures. We implemented the model and ran a series of experiments to show that, in addition to the added functionality, the use of the structural information embedded in SGML documents can improve the effectiveness of document retrieval, compared to the case where no such information is used. We also show that giving a pragmatic preference to a certain element tape of the SGML documents can enhance retrieval effectiveness.

131 citations

Proceedings ArticleDOI
26 Nov 2014
TL;DR: This investigation finds that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective and that it remains unclear which function is best over-all.
Abstract: Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.

131 citations

Proceedings Article
02 Aug 1996
TL;DR: FACT takes a query-centered view of knowledge discovery, in which a discovery request is viewed as a query over the implicit set of possible results supported by a collection of documents, and where background knowledge is used to specify constraints on the desired results of this query process.
Abstract: This paper describes the FACT system for knowledge discovery from text. It discovers associations - patterns of co-occurrence -amongst keywords labeling the items in a collection of textual documents. In addition, FACT is able to use background knowledge about the keywords labeling the documents in its discovery process. FACT takes a query-centered view of knowledge discovery, in which a discovery request is viewed as a query over the implicit set of possible results supported by a collection of documents, and where background knowledge is used to specify constraints on the desired results of this query process. Execution of a knowledge-discovery query is structured so that these background-knowledge constraints can be exploited in the search for possible results. Finally, rather than requiring a user to specify an explicit query expression in the knowledge-discovery query language, FACT presents the user with a simple-to-use graphical interface to the query language, with the language providing a well-defined semantics for the discovery actions performed by a user through the interface.

131 citations

Book ChapterDOI
TL;DR: This work adapts a state-of-the-art text-based document ranking algorithm, the vector-space model instantiated with the TFxIDF ranking rule, to the P2P environment, and develops a heuristic for adaptively determining the set of peers that should be contacted for a query.
Abstract: We consider the problem of content search and retrieval in peer-to-peer (P2P) communities. P2P computing is a potentially powerful model for information sharing between ad hoc groups of users because of its low cost of entry and natural model for resource scaling. As P2P communities grow, however, locating information distributed across the large number of peers becomes problematic. We address this problem by adapting a state-of-the-art text-based document ranking algorithm, the vector-space model instantiated with the TFxIDF ranking rule, to the P2P environment. We make three contributions: (a) we show how to approximate TFxIDF using compact summaries of individual peers' inverted indexes rather than the inverted index of the entire communal store; (b) we develop a heuristic for adaptively determining the set of peers that should be contacted for a query; and (c) we show that our algorithm tracks TFxIDF's performance very closely, giving P2P communities a search and retrieval algorithm as good as that possible assuming a centralized server.

130 citations

Proceedings ArticleDOI
24 Oct 2016
TL;DR: The Noise-Contrastive Estimation approach is extended with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples and achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.
Abstract: We study answer selection for question answering, in which given a question and a set of candidate answer sentences, the goal is to identify the subset that contains the answer. Unlike previous work which treats this task as a straightforward pointwise classification problem, we model this problem as a ranking task and propose a pairwise ranking approach that can directly exploit existing pointwise neural network models as base components. We extend the Noise-Contrastive Estimation approach with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples. Experiments on TrecQA and WikiQA datasets show that our approach achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.

130 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