Paul Huygen


2016

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Two Architectures for Parallel Processing of Huge Amounts of Text
Mathijs Kattenberg | Zuhaitz Beloki | Aitor Soroa | Xabier Artola | Antske Fokkens | Paul Huygen | Kees Verstoep
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents two alternative NLP architectures to analyze massive amounts of documents, using parallel processing. The two architectures focus on different processing scenarios, namely batch-processing and streaming processing. The batch-processing scenario aims at optimizing the overall throughput of the system, i.e., minimizing the overall time spent on processing all documents. The streaming architecture aims to minimize the time to process real-time incoming documents and is therefore especially suitable for live feeds. The paper presents experiments with both architectures, and reports the overall gain when they are used for batch as well as for streaming processing. All the software described in the paper is publicly available under free licenses.