Tracing Inspiration in Proverbial Material: From 'The Royal Dictionary' (1699 & 1729) of Abel Boyer to 'The English-Irish Dictionary' (1732) of Begley-McCurtin.

This metalexicographic study examines the relationship between the proverbial material in The English-Irish Dictionary (1732) of Begley and McCurtin, Abel Boyer’s The Royal Dictionary (First edition 1699, second edition 1729), and Nathaniel Bailey’s An Universal Etymological English Dictionary (1721). It will show, for the first time, that both the English macrostructure and microstructure of the proverbial entries in Begley and McCurtin (1732) were reproduced directly from Boyer’s dictionary and, in spite of claims to the contrary, the impact of Bailey’s (1721) dictionary was negligible. Furthermore, empirical data gleaned from a comparative linguistic analysis of the various editions of The Royal Dictionary prior to 1732, will prove that it was the second official edition (1729) that was used as the framework for The English-Irish Dictionary. A quantitative and qualitative analysis of the nature of the proverbial entries will also outline the various translation strategies that were used to compose the Irish material— particularly literal translation—and show that there are extremely high-levels of borrowings from Boyer (1729), both in terms of the English entries under the lemma, and the French entries in the comment.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.