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Academic Paper


Title: A fast and flexible architecture for very large word n-gram datasets
Author: Michael Flor
Institution: NLP and Speech Group
Linguistic Field: Computational Linguistics
Abstract: This paper presents TrendStream, a versatile architecture for very large word n-gram datasets. Designed for speed, flexibility, and portability, TrendStream uses a novel trie-based architecture, features lossless compression, and provides optimization for both speed and memory use. In addition to literal queries, it also supports fast pattern matching searches (with wildcards or regular expressions), on the same data structure, without any additional indexing. Language models are updateable directly in the compiled binary format, allowing rapid encoding of existing tabulated collections, incremental generation of n-gram models from streaming text, and merging of encoded compiled files. This architecture offers flexible choices for loading and memory utilization: fast memory-mapping of a multi-gigabyte model, or on-demand partial data loading with very modest memory requirements. The implemented system runs successfully on several different platforms, under different operating systems, even when the n-gram model file is much larger than available memory. Experimental evaluation results are presented with the Google Web1T collection and the Gigaword corpus.

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This article appears IN Natural Language Engineering Vol. 19, Issue 1, which you can READ on Cambridge's site or on LINGUIST .



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