Lucene in Action

By Otis Gospodnetic, E. Hatcher, D. Cutting. 2004

875 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/202636f452d551548d16a9742f423a0b81f5e9cc

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Citations in the corpus

Say Anything: A Massively Collaborative Open Domain Story Writing Companion (2008, methodology): “In this work we used Apache Lucene [15], a freely available open source search engine.”
Finding needles in the haystack: Search and candidate generation (2012, methodology): “The first concerns the retrieval model used, where Indri uses a combination of language modeling and inference network for ranking [15], and Lucene leverages tf (term frequency) and idf (inverse document frequency) for scoring relevance [16].”
Recognition and retrieval of mathematical expressions (2011, methodology): “A number of MIR systems implement vector-space models using the popular Lucene(13) [60] indexing and retrieval library, both for indexing entire documents that”
Detecting large-scale system problems by mining console logs (2009, methodology): “To parse the logs, we first compile all message templates into an Apache Lucene [11] reverse index [20], which allows us to quickly associate any log message with the corresponding template.”
Who did What: A Large-Scale Person-Centered Cloze Dataset (2016, methodology): “To find a passage we search the Gigaword dataset using the Apache Lucene information retrieval system (McCandless et al., 2010), using the question sentence as the query.”
Multi-Domain Neural Machine Translation through Unsupervised Adaptation (2017, methodology): “However, since the similarity measure used in Lucene is based on tf-idf counts (Baeza-Yates and Ribeiro-Neto, 2011), it does not consider the order of the words and n-grams in the query and in the retrieved sentences, which is an important aspect for MT model training.”
Toponym resolution in text: annotation, evaluation and applications of spatial grounding (2007, methodology): “The IR engine is used to retrieve the top 1,000 documents for each evaluation query from the collection using the Vector Space Model with the TF*IDF ranking function (Gospodnetić and Hatcher (2005) p.”
A fast Peptide Match service for UniProt Knowledgebase (2013, background): “Received on March 21, 2013; revised on July 5, 2013; accepted on August 14, 2013”
Getting More Out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics (2013, methodology): “Our initial implementation (based on Lucene [53]) generated an index disk footprint on the order of exponential in relation to the source data, and therefore could not scale beyond very small data sets.”