2012/07/18 by Klaus Berberich, Berberich, Klaus, Srikanta Bedathur +1
Computer Science · #Algorithms and Data Compression #Data Mining Algorithms and Applications #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Parallel #and Cluster Computing (cs.DC) #cs.DB #cs.DC #cs.IR
paper · pdf · doi:10.48550/arxiv.1207.4371
arxiv created 2012/07/18 · openalex publication_date 2012/07/18 · arxiv updated 2012/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Statistics about n-grams (i.e., sequences of contiguous words or other tokens in text documents or other string data) are an important building block in information retrieval and natural language processing. In this work, we study how n-gram statistics, optionally restricted by a maximum n-gram length and minimum collection frequency, can be computed efficiently harnessing MapReduce for distributed data processing. We describe different algorithms, ranging from an extension of word counting, via methods based on the Apriori principle, to a novel method Suffix-σthat relies on sorting and aggregating suffixes. We examine possible extensions of our method to support the notions of maximality/closedness and to perform aggregations beyond occurrence counting. Assuming Hadoop as a concrete MapReduce implementation, we provide insights on an efficient implementation of the methods. Extensive experiments on The New York Times Annotated Corpus and ClueWeb09 expose the relative benefits and trade-offs of the methods.