Revisiting Major Discoveries in Linguistic Geometry with Mosaic Reasoning

AbstractIn discovering the nature of the Primary Language of the human brain introduced by J. von Neumann, two components have been investigated. The first component is Linguistic Geometry (LG), the algorithm of optimizing war-fighting strategies. LG's universal applicability in various domains and its power in generating human-like strategies suggested that LG should be a component of the Primary Language. The second component is the algorithm of inventing new algorithms. This paper researches the role of mosaic reasoning, a component of the Algorithm of Discovery, in obtaining the key results in LG such as the algorithms for generating trajectories and zones

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Methods in Psychological Research

Psychologists collect empirical data with various methods for different reasons. These diverse methods have their strengths as well as weaknesses. Nonetheless, it is possible to rank them in terms of different critieria. For example, the experimental method is used to obtain the least ambiguous conclusion. Hence, it is the best suited to corroborate conceptual, explanatory hypotheses. The interview method, on the other hand, gives the research participants a kind of emphatic experience that may be important to them. It is for the reason the best method to use in a clinical setting. All non-experimental methods owe their origin to the interview method. Quasi-experiments are suited for answering practical questions when ecological validity is importa

Cassirer and Steinthal on Expression and the Science of Language

Ernst Cassirer’s focus on the expressive function of language should be read, not in the context of Carnap’s debate with Heidegger, but in the context of the earlier work of Chajim (Heymann) Steinthal. Steinthal distinguishes the expressive form of language, when language is studied as a natural phenomenon, from language as a logical, inferential system. Steinthal argues that language always can be expressed in terms of logical inference. Thus, he would disagree with Heidegger, just as Carnap does. But, Steinthal insists, that is not to say that language, as a natural phenomenon, is exhausted by logic or by the place of terms or relations in inferential structures. Steinthal’s “form” of linguistic “expression” is an early version of Cassirer’s “expressive function” for language. The expressive function, then, should not be seen to place a barrier between Carnap and Cassirer. Rather, Steinthal and Cassirer deal with a question that, as far as I know, Carnap does not address directly: how should philosophers analyze human language as a natural phenomenon, as a part of our expression as animals? And how does that expression determine the semantic categories, kind terms, and other structures that develop within, and characterize, human language itself

Discovering Interpretable Machine Learning Models in Parallel Coordinates

This paper contributes to interpretable machine learning via visual knowledge discovery in parallel coordinates. The concepts of hypercubes and hyper-blocks are used as easily understandable by end-users in the visual form in parallel coordinates. The Hyper algorithm for classification with mixed and pure hyper-blocks (HBs) is proposed to discover hyper-blocks interactively and automatically in individual, multiple, overlapping, and non-overlapping setting. The combination of hyper-blocks with linguistic description of visual patterns is presented too. It is shown that Hyper models generalize decision trees. The Hyper algorithm was tested on the benchmark data from UCI ML repository. It allowed discovering pure and mixed HBs with all data and then with 10-fold cross validation. The links between hyper-blocks, dimension reduction and visualization are established. Major benefits of hyper-block technology and the Hyper algorithm are in their ability to discover and observe hyper-blocks by end-users including side by side visualizations making patterns visible for all classes. Another advantage of sets of HBs relative to the decision trees is the ability to avoid both data overgeneralization and overfitting.Comment: 8 pages, 18 figure