## 1 Introduction
A set of defined concept types of a source model (or their properties) can be mapped to a set of defined concept types of a target model (or their properties). * It supports consistency of concept mappings within a single model or across multiple models based on UML or UML profiles. In the M2M transformation specifications, one can specify concept mappings within a single model or among different models expressed using the same or different modeling languages, including UML, BPMN, SBVR, SoaML, and others implemented as UML profiles. * It supports reuse of M2M transformations. Out of the set of defined transformation specifications, a particular transformation can be enacted by the transformation engine depending on the conditions of the actual situation related to the source model elements and/or drag-and-drop actions performed. This increases the reuse of libraries of transformation specifications across multiple domains. * It assures traceability between the source model elements and the transformed target model elements [20]. The NLP extension introduced in this paper augments this list with the following characteristics:* It enables conditional transformations, which allow us to specify and perform more advanced transformation scenarios based on different conditions or constraints. * It enhances tolerance to various concept-naming conventions used in the source model when generating valid results: Advanced natural language-based parsing of expressions provides an additional means to acquire valid results (i.e., properly named target concepts) compared to our previously developed M2M transformation solution without NLP support. If required, one may specify and perform conditional processing depending on different, sometimes poor, concept naming conventions, e.g., one can properly process use case concepts named by both a verb + noun (e.g., \"Issue invoice\") and a single verb or noun (e.g., \"Issue\", \"Invoice\"). * It reduces the level of redundancy in the target model: If required, one may simplify the target model by combining concepts that have identical or synonymous meanings. This is achieved by identifying synonymous forms and abbreviations. The overall methodological background of the partial M2M transformation approach together with the aforementioned experimentation on the usability, user acceptance, and other relevant criteria was already extensively discussed in our previous research paper [14]. With that being done, our main objective in this paper was to present the main conceptual and engineering aspects of the developed NLP-enhanced extension of the existing partial M2M transformation solution and introduce a set of detailed application use cases on the transformation specification level. This enables us to explicitly represent the aforementioned beneficial capabilities of the NLP-enhanced M2M transformation solution in the scope of model-driven systems engineering. The paper is structured as follows: Section 2 provides an overview of the related work in intersecting fields. In Section 3, an illustrative example is introduced, explaining the basic scenario of applying partial M2M transformation in the context of system design. In Section 4, we present an extension to the previously developed metamodel of M2M transformation together with the actual implementation details. Section 5 mainly focuses on a set of NLP operations that we brought into our M2M transformation method. Section 6 extensively describes a set of main M2M transformation use cases utilizing new capabilities introduced by the developed NLP extension, while Section 7 presents and discusses the experimental results. Finally, Section 8 offers conclusions and future work insights.