A probabilistic model of information retrieval: development and comparative experiments - Part 2

By Karen Spärck Jones, S. Walker, Stephen E. Robertson. Information Processing & Management, 2000

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/92af2d0fd422d8ff70fce1c748e6b581d52d2c35

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, neutral): “Some of the early work in probabilistic IR is reported by Sparck Jones, Robertson, and Walker [179, 197], Ponte and Croft [173] and Turtle and Croft [201].”
Effective Pattern Discovery for Text Mining (2012, positive): “The values of k1 and b are set as 1.2 and 0.75,respectively,accordingtothesuggestionin[ 47 ]and[48].”
Inferring Query Performance Using Pre-retrieval Predictors (2004, methodology): “For the parameters k1 and k3, we use the standard setting of [14], i.”
Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering (2019, result): “We also empirically test for each question word q i , summing f ext ( q i , a j | q , a ) of all the answer words a j instead of using max pooling, which leads to similar results but takes longer time to train.”
Content-based visual search learned from social media (2012, methodology): “Since k1 does not affect ranking for single-word queries, we set k1 to 2, a common choice in text retrieval [53].”
Novelty and diversity in information retrieval evaluation (2008, methodology): “Table 2 presents the results of executing the target as a query using a typical implementation of the BM25 scoring formula [26].”
An Updated Duet Model for Passage Re-ranking (2019, methodology): “We adopt the Robertson-Walker definition of IDF [Jones et al., 2000] normalized to the range [0, 1].”
Global term weights for document retrieval learned from TREC data (2001, positive): “This formula can be given some justiﬁcation as an approximation to relevance-based probabilistic weighting by making simplifying assumptions [3, 6].”
CORD-19: The Covid-19 Open Research Dataset (2020, methodology): “A vast majority of retrieval tools currently use standard ranking methods, such as BM25 (Jones et al., 2000).”
Query Expansion Techniques for Information Retrieval: a Survey (2017, methodology): “[134] present a probabilistic model for calculating document matching score and came up with superior results on TREC Programme collections.”