Improving Academic Natural Language Processing Infrastructures Utilizing Cluster Computation

In light of widespread digitization endeavors and ever-growing textual data generation, developing efficient academic Natural Language Processing (NLP) infrastructures, which can deal with large amounts of data, is of particular importance. Novel computation technologies allow tools that support big data and heavy computation while performing timely and cost-effective data processing. This development has led researchers to demand that knowledge be extracted from ever-increasing textual data before it is outdated. Cluster computation is a modern technology for handling big data efficiently. It provides distribution of computing and data over a number of machines in a cluster, as well as efficient use of resources, which are key requirements to process big data in a timely manner. It also assures applications’ high availability and fault tolerance, which are fundamental concerns when dealing with vast amounts of data. In addition, it provides load balancing of data during the execution of tasks, which results in optimal use of resources and enhances efficiency. Data-oriented parallelization is an effective solution to enable the currently available academic NLP infrastructures to process big data. This approach offers a solution to parallelize the NLP tools which comprise identical non-complicated tasks without the expense of changing NLP algorithms. This thesis presents the adaption of cluster computation technology to academic NLP infrastructures to address the notable features that are essential to process vast quantities of text materials efficiently, in terms of both resources and time. Apache Spark on top of Apache Hadoop and its ecosystem have been utilized to develop a set of NLP tools that provide a distributed environment to execute the NLP tasks. Many experiments were conducted to assess the functionality of the designated strategy. This thesis shows that using cluster computation technology and data-oriented parallelization enables academic NLP infrastructures to execute large amounts of textual data in a timely manner while improving the performance of the NLP tools. Moreover, these experiments provide information that brings a more realistic and transparent estimation of workflows’ costs (required hardware resources) and execution time, along with the fastest, optimum, or feasible resource configuration for each individual workflow. This knowledge can be employed by users to trade-off between run-time, size of data, and hardware, and it enables them to design a strategy for data storage, duration of data retention, and delivery time. This has the potential to enhance researchers’ satisfaction when using academic NLP infrastructures. The thesis also shows that a cluster computation approach provides the capacity to adapt NLP services with JIT delivery systems. The proposed strategy assures the reliability and predictability of the services, which are the main characteristics of the services in JIT delivery systems. Defining the relevant parameters, recording the behavior of the services, and analyzing the generated data resulted in the provision of knowledge that can be utilized to create a service catalog—a fundamental requirement for the services in JIT delivery systems—for each service offered. This knowledge also helps to generate the performance profiles for each item mentioned in the service catalog and to update them continuously to cover new experiments and improve service quality