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Existing educational tools for language processors have varying capabilities. There is no single tool that covers every aspect of language processors. As a result, there is a possibility that the educator and students...
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Existing educational tools for language processors have varying capabilities. There is no single tool that covers every aspect of language processors. As a result, there is a possibility that the educator and students will need multiple tools, switching between different specification notations, organization and interpretation of outputs, which could result in a steep learning curve. PAMOJA is a Java-based component framework with a broader scope - it supports construction of grammar-aware applications using the rapid application development style found in modern IDEs such as NetBeans. This paper investigated the possibility of using PAMOJA to develop educational tools for language processing courses. We conducted two major studies. The first study, summarized six design considerations, identified in the literature, for language processor educational tools. Then, we identified three example applications and demonstrated how to construct tools for them using PAMOJA. These include building and visualizing lexical scanners and parsers, as well as constructing a front-end for a software language, using a subset of Java language as an example. The second study evaluated the PAMOJA approach in relation to the identified design considerations, and by analyzing student and educator perceptions. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of language processor teaching tools and learning language processors. (c) 2023 Elsevier B.V. All rights reserved.
We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard supervised parsing objective function wit...
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ISBN:
(纸本)9781937284114
We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard supervised parsing objective function with additional loss-functions, either based on intrinsic parsing quality or task-specific extrinsic measures of quality. Our empirical results show how this approach performs for two dependency parsing algorithms (graph-based and transition-based parsing) and how it achieves increased performance on multiple target tasks including reordering for machine translation and parser adaptation.
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