Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer

By Jimmy J. Lin, Chris Dyer. International Conference on Computational Logic, 2010

321 citations recorded by the source corpus.

This half-day tutorial introduces participants to data-intensive text processing with the MapReduce programming model [1], using the open-source Hadoop implementation. The focus will be on scalability and the tradeoffs associated with distributed processing of large datasets. Content will include general discussions about algorithm design, presentation of illustrative algorithms, case studies in HLT applications, as well as practical advice in writing Hadoop programs and running Hadoop clusters.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/508732db9cbe6cbf6dc1a5451090a0cec950a7f4

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In Management Science and Operations Research

Citations in the corpus

Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber-physical convergence (2012, unknown): “Recent evidence in fact suggests that employing even relatively simple non-parametric density approximationmodels on such data sets can provide significant advantage [34].”
Large Scale Sentiment Analysis on Twitter with Spark (2016, methodology): “Distribution of this paper is permitted under the terms of the Creative Commons license CCby-nc-nd 4.0”
Parallel data processing with MapReduce: a survey (2012, background): “Lin et al propose a scheme called ”schimmy” to save I/O cost during reduce-side join[75].”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “Join methods can be roughly classified into two groups: Map-side join [270] and Reduce-side join [271].”
A survey on question answering technology from an information retrieval perspective (2011, background): “In this case one may consider MapReduce [67] and ’just-in-time’ semantic representations [134].”
Syntactically Guided Neural Machine Translation (2016, background): “translation grammars and language models with very large vocabularies (Heafield et al., 2013; Lin and Dyer, 2010).”
Text: now in 2D! A framework for lexical expansion with contextual similarity (2013, background): “For related works on the computation of distributional similarity, see Lin (1998), Gorman and Curran (2006), Lin and Dyer (2010), inter alia.”
Neural Machine Translation: A Review (2019, unknown): “rent encoder in parallel, and Lin et al. (2018b) who equipped a recurrent decoder with a convolutional decoder to provide global target-side context.”
Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling (2012, background): “This algorithm is a variant of what is commonly known as a reduce-side join in MapReduce (Lin and Dyer, 2010), where (de, df ) serves as the join key.”