Now showing 1 - 3 of 3
  • Publication
    An Open Vocabulary Semantic Parser for End-User Programming using Natural Language
    (IEEE, 2018-01) ;
    Freitas, André
    ;
    The ability to automatically interpret natural language commands and actions has the potential of freeing up end-users to interact with software artefacts without the syntactic, vocabulary and formal constraints of a programming language. As most semantic parsers for end-user programming have been operating under a restricted vocabulary setting, it is unclear how these approaches perform over conditions of high semantic heterogeneity (e.g. in an open vocabulary). As the generation of annotated data is costly and time-consuming, models that effectively address complex learning problems constrained under the assumption of small annotated data sets are highly relevant. In this paper, we propose a semantic parsing approach to map natural language commands to actions from a large and heterogeneous frame set trained under a small set of annotated data. The semantic parsing approach uses the combination of semantic role labelling, distributional semantics geometric features and semantic pivoting in order to address the semantic matching problem in an open vocabulary setting.
    Scopus© Citations 8
  • Publication
    Mathematical Foundations of Data Science
    (Springer Cham, 2023-03-13)
    Hrycej, Tomas
    ;
    ; ;
    This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used? Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success. Topics and features: Focuses on approaches supported by mathematical arguments, rather than sole computing experiences Investigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from them Considers key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithms Examines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problem Addresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrization Investigates the mathematical principles involves with natural language processing and computer vision Keeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire book Although this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations “beyond” the sole computing experience.