A cloud-hosted MapReduce architecture for syntactic parsing

Woldemariam, YD; Pletschacher, S; Clausner, C; Bass, JM

Abstract

Syntactic parsing is a time-consuming task innatural language processing particularlywherea largenumber of text files are beingprocessed. Parsingalgorithms are conventionally designed to operate on a single machine in a sequential fashionand, as a consequence, failto benefit from high performance and parallel computing resources available on the cloud.We designed and implemented a scalable cloud-based architecture supporting parallel and distributed syntactic parsing for large datasets. The main architecture consists of asyntactic parser(constituency and dependency parsing)and a MapReduceframework running onclusters of machines.The resulting cloud-based MapReduce parsing is able to builda map where syntactic trees of the same input file have the same keyand collect into a singlefile containing sentences along with their corresponding trees.Ourexperimental evaluation showsthat the architecture scales wellwith regard to number or processing nodes and number of cores per node.In the fastest tested cloud-based setup, the proposed design performs 7times faster when compared to a localsetup. In summary, this study takes an important step toward providing and evaluating a cloud-hostedsolution for efficient syntactic parsingof natural language data sets consisting of a large number of files.

Citation

Woldemariam, Y., Pletschacher, S., Clausner, C., & Bass, J. (2019). A cloud-hosted MapReduce architecture for syntactic parsing. In Kallithea, Greece. https://doi.org/10.1109/SEAA.2019.00024

• Conference Name: Euromicro Conference on Software Engineering and Advanced Applications
• Start Date: Aug 28, 2019
• End Date: Aug 30, 2019
• Acceptance Date: May 7, 2019
• Online Publication Date: Nov 21, 2019
• Publication Date: Nov 21, 2019
• Deposit Date: Jul 3, 2019
• Publicly Available Date: Jul 3, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: Kallithea, Greece
• DOI: https://doi.org/10.1109/SEAA.2019.00024