Semantic transparency modulates the semantic perception of morphemes: Evidence from RSA of BOLD signals

There is no converging evidence on how a word's semantic transparency affects morphemes' potential semantic activation. The inconsistent results may be due to the limitation of traditional univariate analyses, in which the semantic transparency was treated as discrete categories. In the current study, Chinese two-character words were used as stimuli and functional magnetic resonance imaging (fMRI) techniques were combined with a priming paradigm. Unlike most previous studies, the multivariate representation similarity analysis (RSA) was used to treat semantic transparency as a continuous variable. The RSA results showed that widespread regions in the frontal-parietal-temporal network represent the semantic perception of characters in all words and transparent words, but no brain areas were identified in opaque words. Unlike RSA results, univariate analyses showed no significant difference between the opaque and transparent words. These results suggest that RSA is more suitable to examine the neural mechanism related to continuous variables such as semantic transparency.

Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning

BACKGROUND

Given the increasing number of dementia patients worldwide, a new method was developed for machine learning models to identify the 'latent needs' of patients and caregivers to facilitate patient/public involvement in societal decision making.

METHODS

Japanese transcribed interviews with 53 dementia patients and caregivers were used. A new morpheme selection method using Z-scores was developed to identify trends in describing the latent needs. F-measures with and without the new method were compared using three machine learning models.

RESULTS

The F-measures with the new method were higher for the support vector machine (SVM) (F-measure of 0.81 with the new method and F-measure of 0.79 without the new method for patients) and Naive Bayes (F-measure of 0.69 with the new method and F-measure of 0.67 without the new method for caregivers and F-measure of 0.75 with the new method and F-measure of 0.73 without the new method for patients).

CONCLUSION

A new scheme based on Z-score adaptation for machine learning models was developed to predict the latent needs of dementia patients and their caregivers by extracting data from interviews in Japanese. However, this study alone cannot be used to assign significance to the adaptation of the new method because of no enough size of sample dataset. Such pre-selection with Z-score adaptation from text data in machine learning models should be considered with more modified suitable methods in the near future.

The morph as a minimal linguistic form

This paper makes a terminological proposal: that the old term morph can be used for a minimal linguistic form. Many linguists (not only morphologists) need such a term, because we often refer to minimal linguistic forms, but the various terms used by linguists in roughly this meaning are either unclear or do not refer to forms. The term "morpheme" has three rather different meanings, and other terms such as "vocabulary item" are too abstract. The term "morph" can be used as the basis for defining other widely used terms such as "root", "prefix", and "suffix", which are currently often defined as kinds of "morphemes". It can also serve as the basis for a clearer definition of suppletion (involving suppletive morph sets) and morph variants, thus avoiding the confusions surrounding the term "allomorph(y)".