Terrier : A High Performance and Scalable Information Retrieval Platform

By Iadh Ounis, Gianni Amati, Vassilis Plachouras, Ben He, Craig Macdonald, Christina Lioma

Subfields: Information Retrieval & Search, ML Systems & Hardware

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Citations in the corpus

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].”
Graph-of-word and TW-IDF: new approach to ad hoc IR (2013, methodology): “We used Terrier version 3.5 [20] to index, retrieve and evaluate the retrieval models over the TREC collections.”
PACRR: A Position-Aware Neural IR Model for Relevance Matching (2017, background): “html (4)Terrier (Ounis et al., 2006) version without filtering spam documents (5)https://github.”
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval (2017, unknown): “In the case of cascade k-max pooling, we uniformly divide [ 0% , 100% ] into n c parts, e.g., with n c = 5 we have cpos = [ 20% , 40% , 60% , 80% , and 100% ] .”
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].”
Old dogs are great at new tricks: column stores for ir prototyping (2014, unknown): “5), and Terrier [12] (v3.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “[11]” In particular, query log mining is concerned with all those techniques aimed at discovering interesting patterns from query logs of web search engine with the purpose of enhancing either effectiveness or efficiency of an online service provided through the web.”
From MAXSCORE to Block-Max Wand: The Story of How Lucene Significantly Improved Query Evaluation Performance (2020, background): “The tradition continues to this day, with Lemur/Indri [12, 13] and Terrier [14, 8] being the most successful examples of open-source academic search engines, still popular with many researchers today.”
Exploiting query reformulations for web search result diversification (2010, methodology): “The Terrier IR platform(2) [21] is used for both indexing and retrieval, with Porter’s stemmer and”
The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017 (2017, methodology): “An incomplete list includes SMART [18], Lemur/Indri [15, 16], Galago [8], Terrier [14, 17], ATIRE [20], Ivory [12], JASS [13], MG4J [6], Wumpus [21], and Zeair [25].”
An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures (2017, background): “To address this challenge, many academic research groups have built and shared open-source search engines with the broader community—prominent examples include Lemur/Indri [9, 10] and Terrier [7, 13].”
Design trade-offs for search engine caching (2008, methodology): “We perform the experiments using an optimized version of Terrier [Ounis et al. 2006], for both indexing documents and processing queries, on a single machine with a Pentium 4 at 2GHz and 1GB of RAM.”
Content-Based Weak Supervision for Ad-Hoc Re-Ranking (2017, methodology): “e 2012–14 querylikelihood baselines (Terrier [10] version) from Trec2 serve as the QL baseline rankings.”
Deep Reinforced Query Reformulation for Information Retrieval (2020, methodology): “We index MSMARCO using the Terrier IR platform [28], removing standard stopwords and applying Porter stemming.”
Integrating and Evaluating Neural Word Embeddings in Information Retrieval (2015, methodology): “Both indexing and retrieval was implemented using the Terrier IR toolkit [24].”