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Now-a-days the chatbot development has been moving from the field of Artificial-Intelligence labs to the desktops and mobile domain experts. In the fastest growing technology world, most smartphone users spend major time in the messaging apps such as Facebook messenger. A chatbot is a computer program that uses messaging channels to interact with users using natural Languages. Chatbot uses appropriate mapping techniques to transform user inputs into a relational database and fetch the data by calling an existing API and then sends an appropriate response to the user to drive its chats. Drawbacks include the need to learn and use chatbot specific languages such as AIML (Artificial Intelligence Markup Language), high botmaster interference, and the use of non-matured technology. In this project, Facebook messenger based chatbot is proposed to provide domain independent, an easy to use, smart, scalable, dynamic and conversational agent in order to get information about CSUSB. It has the unique functionalities which identify user interactions made by their natural language, and the flawless support of various application domains. This provides an ample of unique scalabilities and abilities that will be evaluated in the future phases of this project.

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.

This thesis seeks to investigate the influence and applicability of three technical writing principles across electronic maintenance manuals in the aerospace defense industry: military standard (MIL-STD) guidelines, plain language, and audience scope. Aerospace defense technical writers are liaisons tasked with coherent communication on advanced technological developments for technicians maintaining equipment. Their primary responsibility involves synthesizing specialized content from subject matter experts to draft comprehensive instructions for personnel safety and product sustainment during critical military operations. Current literature insufficiently examines the significance between aerospace defense technical documents and product performance following routine maintenance. Poorly composed manuals contribute to technician misinterpretation or disregard due to convoluted procedures and disorganized appearances increasing malfunction probabilities. Writing-based MIL-STDs and Simplified English emerged as efforts to mitigate understanding obstructs amongst domestic and international novice technicians. Maintenance manuals must conform to governmental guidelines including product liability laws, cultural variables, and audience expectations. Interview findings with two practitioners each from a different aerospace defense company supports the prediction that technical writing theories considerably impacts maintenance manual quality and recipience throughout the aerospace defense industry.

In the field of Natural Language Processing, word-formation is under-resourced comparing to inflectional morphology. Moreover, the existing resources capturing word-formation differ in many aspects. This thesis aims to review existing language resources for word-formation across languages and to unify them to a common data structure and file format. Basic notions of word-formation are followed by a review of existing language resources and their comparison in both quantitative and qualitative aspects. In the core part of the thesis, the harmonisation process is presented. Design decisions on the unification procedure are presented, and the selection of the resources to unify is described. The resources are unified to the rooted tree data structure and stored in a lexeme-based file format, which is already used in DeriNet 2.0. The procedure applies supervised machine learning model and the Maximum Spanning Tree algorithm. While the model scores word-formation relations, the MST algorithm uses the scores for identifying the rooted tree structure in each word-formation family. The resulting collection of harmonised resources covering 20 European languages was published under the title 'Universal Derivations' (UDer).