Distances derived from word embeddings can measure a range of gradational relations--similarity, hierarchy, entailment, and stereotype--and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation…

Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…

Purpose: Following the Rehabilitation Treatment Specification System (RTSS) framework, the current study investigated the active ingredients in the modified semantic feature analysis (mSFA) targeting either noun or verb retrieval in Mandarin-English bilingual adults with aphasia (BWA). Method: Twelve Mandarin-English BWA completed mSFA treatment…

Purpose: ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia's naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as well as semantic, phonological, and morphological similarity to the target) are important for…

The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder-decoder architectures have advanced traditional KGQG approaches. However, these…

Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Background: In the context of large-scale educational assessments, the effort required to code open-ended text responses is considerably more expensive and time-consuming than the evaluation of multiple-choice responses because it requires trained personnel and long manual coding sessions. Aim: Our semi-supervised coding method eco (exploring…

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…

The conjoint-recognition model (CRM) implements fuzzy-trace theory's opponent process conception of false memory. Within the family of measurement models that separate the memory effects of recollection and familiarity, CRM is the only one that accomplishes this for false as well as true memory. We assembled a corpus of 537 sets of…

Most printed Chinese words are compounds built from the combination of meaningful characters. Yet, there is a poor understanding of how individual characters contribute to the recognition of compounds. Using a megastudy of Chinese word recognition (Tse et al., 2017), we examined how the lexical decision of existing and novel Chinese compounds was…

Unfolding models are frequently used to develop scales for measuring attitudes. Recently, unfolding models have been applied to examine rater severity and accuracy within the context of rater-mediated assessments. One of the problems in applying unfolding models to rater-mediated assessments is that the substantive interpretations of the latent…

We examined whether word recognition accuracy and latency of words children encounter during primary school across the upper primary school grades can be predicted from word form (word length, mean Levenshtein distance, and mean frequency of neighbors), word meaning (free association network markers) and word exposure (corpus frequency and…

Purpose: The purpose of this study was to investigate the effectiveness of a self-administered naming treatment for one individual, B.N., presenting with semantic variant primary progressive aphasia (svPPA) and a history of traumatic brain injury (TBI). Method: Naming treatment included components of Lexical Retrieval Cascade Treatment and was…

Previous work has ostensibly shown that readers rapidly adapt to less predictable ambiguity resolutions after repeated exposure to unbalanced statistical input (e.g., a high number of reduced relative-clause garden-path sentences), and that these readers grow to disfavor the a priori more frequent (e.g. main verb) resolution after exposure (Fine,…