Towards an Efficient and Effective Search Engine

By Andrew Trotman, Xiangfei Jia, Matt Crane. 2012

51 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

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In Information Retrieval & Search

Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, neutral): “, 2012], ATIRE [Trotman et al., 2012], Ivory [Lin et al.”
Anserini (2018, background): “An incomplete list includes Lemur/Indri [38, 39], Galago [15], Terrier [35, 42], ATIRE [51], Ivory [30], JASS [31], MG4J [12], [60, 61].”
Anytime Ranking for Impact-Ordered Indexes (2015, methodology): “Instead of implementing a complete search engine, we use inverted indexes built by the ATIRE system [18], which saved us from having to write a separate indexer.”
Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format (2020, methodology): “’s BM25 ranking using standard metrics: AP (at rank 1000) and P@30 for Robust04, NDCG@10 and ERR@10 for ClueWeb12B. In all cases we set k1 = 0.9 and b = 0.4, per the recommendations of Trotman et al. [13]. These results are shown in Table 2. In the top block of the table, we present figures from eac”
Query Driven Algorithm Selection in Early Stage Retrieval (2017, methodology): “ATIRE [50] was used to parse and index the ClueWeb09B collection, which was stopped using the default Indri stoplist, and stemmed using an s-stemmer.”
Improvements to BM25 and Language Models Examined (2014, methodology): “We implemented the functions using the ATIRE search engine [27], the source code of which is publicly available along with our extensions.”
A Comparison of Document-at-a-Time and Score-at-a-Time Query Evaluation (2017, methodology): “We factor out these differences by using the ATIRE system [35] to first construct a master index, from which all our individual experiments derive.”
Which BM25 Do You Mean? A Large-Scale Reproducibility Study of Scoring Variants (2020, methodology): “ATIRE [10] implements the IDF component of BM25 as log (N/df t ), which also avoids negative values.”
Toward Reproducible Baselines: The Open-Source IR Reproducibility Challenge (2016, methodology): “In total, developers from seven open-source systems participated (in alphabetical order): ATIRE [16], Galago [6], Indri [10,12], JASS [9], Lucene [2], MG4J [3], and Terrier [14].”
Assessing efficiency–effectiveness tradeoffs in multi-stage retrieval systems without using relevance judgments (2015, methodology): “Language model runs we use the default parameters; and for Okapi BM25 runs we use k1 1⁄4 0:9; b 1⁄4 0:4; k3 1⁄4 0, since these values consistently give better results than the default parameters (Trotman et al. 2012).”