A Toolkit for Analyzing Strategy Use in Phonemic Verbal Fluency Tasks preprint

The phonemic verbal fluency task is a common cognitive assessment of language and executive functioning which asks participants to list as many words as they can that begin with a given letter. Verbal fluency tasks are widely used to identify deficits in verbal fluency, which have been associated with disorders such as schizophrenia and dementia. Verbal fluency tasks are scored by the number of correct responses, however analysis of “clusters” of related words within a response list can give insights into the cognitive strategies used by participants. Unfortunately, manual word cluster analysis is time and labor intensive and inconsistent, since raters may cluster words differently depending on how they themselves have phonetically categorized the words. We present an automated pipeline for quantification of strategy use in the phonemic verbal fluency task, “LetterVF”. LetterVF is a python module (i.e., a script containing useful functions, which can be imported and used in other scripts) that uses a pronunciation dictionary to convert verbal fluency task data items into lists of phonemes, which can be analyzed to identify clusters of words that share similarities in any of several clustering categories. Additionally, LetterVF contains useful functions for identifying intrusions (words which do not follow the rules for the task), identifying perseverations (responses repeated within the same trial), counting the number of cluster switches in a list, and calculating the average size of clusters for a list. Analysis of data from 50 participants’ verbal fluency task responses indicated that analysis using LetterVF yields accuracy and consistency on par with manual analysis. Our hope is that this tool will allow researchers to get more out of their datasets, and explore new topics related to cognitive strategy use, such as how strategies change with age and differences in strategies between experimental groups.

Locating Adaptive Agency

Chapter 2 identifies, locates, and disambiguates adaptive agency within the early Chinese textual horizon. It deals with three problems: (1) the notion of adapting is stable, but not always well-delimited or defined; (2) adapting is either expressed through a word cluster or articulated without an attributed term, which makes it more difficult to identify; and (3) adaptive agency must be demarcated from similar notions also pervasive in early Chinese intellectual discourses and their modern scholarly studies. Beyond expounding the philological basis of the research, the section “What Is Not Adapting” clarifies the difference between adapting, flexibility, reliance, balancing, conforming, and spontaneity.

Aristotle’s Vocabulary of Pain

This paper examines Aristotle’s vocabulary of pain, that is the differences and relations of the concepts of pain expressed by (near-)synonyms in the same semantic field. It investigates what is particularly Aristotelian in the selection of the pain-words in comparison with earlier authors and specifies the special semantic scope of each word-cluster. The result not only aims to pin down the exact way these terms converge with and diverge from each other, but also serves as a basis for further understanding Aristotle’s philosophical conception of pain.

ENHANCEMENT OF TEXT BASED EMOTION RECOGNITION PERFORMANCES USING WORD CLUSTERS

Human Computer Interaction (HCI) researches the use of computer technology mainly focused on the interfaces between human users and computers. Expression of emotion comprises of challenging style as it is produced with plaint text and short messaging language as well. This research paper investigates on the overview of emotion recognition from various texts and expresses the emotion detection methodologies applying Machine Learning Approach (MLA). This paper recommends resolving the problem of feature meagerness, and largely improving the emotion recognition presentation from short texts by achieving the three aims: (I) The representing short texts along with word cluster features, (II) Presenting a narrative word clustering algorithm, and (iii) Making use of a new feature weighting scheme of the Emotion classification. Experiments were performed for the classifying the emotions with different features and weighting schemes, on the openly available dataset. We have used the word clusters in place of unigrams as features, the micro-averages of accuracy have been found to be enhanced by more than three percentage, which suggests that the overall accuracy value of the text emotion classifier has been improved. All the macro-averages were enhanced by more than one percentage, which suggests that the word cluster feature can advance the generalization potential of the emotion classifier. The experimental results suggest that the text words cluster features and the proposed weighting scheme can moderately resolve the problems of the emotion recognition performance and the feature sparseness.