Topic
Rule-based machine translation
About: Rule-based machine translation is a research topic. Over the lifetime, 8804 publications have been published within this topic receiving 240581 citations.
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TL;DR: A hybrid approach is presented, which integrates an example-pattern-based method and a rule- based method, to the design and implementation of an English-Chinese machine translation system, which has high translation speed and accuracy.
Abstract: This paper presents a hybrid approach, which integrates an example-pattern-based method and a rule-based method, to the design and implementation of an English-Chinese machine translation system. It focuses discussion on language model , knowledge base, design ideas and implementation strategies. Our system has been tested based on requirement details listed in the Outlines for Automatic Evaluation of Machine Translation constituted by National Hi-Tech Project 863, and compared with the Huajian system. Experiment results indicate that our system has high translation speed and accuracy.
TL;DR: In this article , a corpus of seven grammars published in Quebec, two made for elementary school, three for secondary school and two for teaching studies at university, is analyzed.
Abstract: The study focuses on verbal and sentence complements as they are viewed by school grammar in Quebec. It analyzes a corpus of seven grammars published in Quebec, two made for elementary school, three for secondary school and two for teaching studies at university. The grammars for school have been approved by the ministry of education. The paper analyzes the syntactic tests as they are used in the seven books in the corpus, but also other angles chosen to define functions relating to the verb. Finally, the paper proposes to give less importance to tests in favor of a better consideration of semantics.
31 Jul 2022
TL;DR: An experiment is proposed to analyze the support given by GrameStation during the running of games modeled as Graph Grammars in order to facilitate understanding of this formalism.
Abstract: GrameStation is a game engine based on Graph Grammar, a formal language used to describe systems and verify properties. Despite being intuitive, understanding a Graph Grammar may not be trivial for those who do not have previous experiences with this formalism. Therefore, we propose an experiment to analyze the support given by GrameStation during the running of games modeled as Graph Grammars in order to facilitate this understanding. We analyzed three groups of people with different levels of knowledge about Graph Grammar. They played two games in the platform and answered a questionnaire. It was found that the greatest difficulty was in understanding how to make the mappings to progress in the game and, as a consequence, the addition of tutorials in the platform was mentioned by several participants.