Self-move and Other-move: Quantum Categorical Foundations of Japanese

The purpose of this work is to contribute toward the larger goal of creating a Quantum Natural Language Processing (QNLP) translator program. This work contributes original diagrammatic representations of the Japanese language based on prior work that accomplished on the English language based on category theory. The germane differences between the English and Japanese languages are emphasized to help address English language bias in the current body of research. Additionally, topological principles of these diagrams and many potential avenues for further research are proposed. Why is this endeavor important? Hundreds of languages have developed over the course of millennia coinciding with the evolution of human interaction across time and geographic location. These languages are foundational to human survival, experience, flourishing, and living the good life. They are also, however, the strongest barrier between people groups. Over the last several decades, advancements in Natural Language Processing (NLP) have made it easier to bridge the gap between individuals who do not share a common language or culture. Tools like Google Translate and DeepL make it easier than ever before to share our experiences with people globally. Nevertheless, these tools are still inadequate as they fail to convey our ideas across the language barrier fluently, leaving people feeling anxious and embarrassed. This is particularly true of languages born out of substantially different cultures, such as English and Japanese. Quantum computers offer the best chance to achieve translation fluency in that they are better suited to simulating the natural world and natural phenomenon such as natural speech. Keywords: category theory, DisCoCat, DisCoCirc, Japanese grammar, English grammar, translation, topology, Quantum Natural Language Processing, Natural Language ProcessingComment: 104 pages; 31 figures; 9 table

Natural Language Based Object-Oriented Software Modelling

Deriving useful information from natural language has been a task of much relevance for fields ranging from machine translation, software modelling, and artificial intelligence and so on. Sufficient literature is available on utilisation of grammatical inference in object oriented software modelling. The major advancements in this field along with the challenges faced by researchers as well as practitioners have been outlined. An amalgamation of ideas taken from existing theories and models along with proposed methodology has been worked out so as to utilise natural language text in the field of object oriented analysis and design. The very first step of Natural Language (NL) text processing is Parts-of-Speech (POS) tagging. Grammatical rules, some already existing and some deduced through careful observation of NL structures has been extensively discussed and implemented. After appropriate tagging the words to their respective parts of speech the objective is to recognise the classes among them. The classes along with their attributes and methods were listed out. These classes essentially are identified as part of the major functionalities in an information system. The information system consists of requirement specification given by clients for their target software. Comprehending client specification is a time consuming process. Therefore analysing classes from the specification provided becomes mandatory. Several ambiguities and redundancy in class identification were faced and were effectively resolved. Final classes from the given requirement specification were found out. Subsequently the knowledge acquired from the same is put to use while analysing these functionalities through various UML (Unified Modelling Language) diagrams. There are several UML tools that serve the purpose of drawing these diagrams. But the motive is to make the entire process of deriving the UML diagrams in a logical and automated manner

A novel case tool based on pre-conceptual schemas for automatically obtaining uml diagrams

Assistance is provided, in software development process, to Analysts in drawing UML diagrams and others by means of CASE tools. However, the task of the Stakeholder discourse understanding, a previous process in diagram drawing, is not supported by traditional CASE tools. In order to complete this task, Natural Language Processing has proposed a new kind of CASE tools, including both natural language interpretation and UML diagrams generation. We introduce, in this paper, UNC–Diagrammer, a novel CASE tool for graphically representing the Stakeholder discourse by means of Preconceptual Schemas. We also show that UNC-Diagrammer is capable of automatically transforming Pre-conceptual Schemas into three UML 2.0 diagrams. We finally demonstrate the use of UNC–Diagrammer through an example

Improving quality of use case documents through learning and user interaction

Use cases are widely used to capture user requirements based on interactions between different roles in the system. They are mostly documented in natural language and sometimes aided with graphical illustrations in the form of use case diagrams. Use cases serve as an important means to communicate among stakeholders, requirement engineers and system engineers as they are easy to understand and are produced early in the software development process. Having high quality use cases are beneficial in many ways, e.g., in avoiding inconsistency/incompleteness in requirements, in guiding system design, in generating test cases. In this work, we propose an approach to improve the quality of use cases using techniques including natural language processing and machine learning. The central idea is to discover potential problems in use cases through active learning and human interaction and provide feedbacks in natural language. We conduct user studies with a real-world use case document. The results show that our method is helpful in improving use cases with a reasonable amount of user interaction.No Full Tex