Assessing lexical-semantic naming deficits using an automated machine learning approach

After left hemisphere stroke, 20-50% of people experience language deficits, including difficulties in naming. Naming errors that are semantically related to the intended target (e.g., producing “violin” for picture HARP) indicate a potential impairment in accessing knowledge of word forms and their meanings. Understanding the cause of naming impairments is crucial to better modeling of language production as well as for tailoring individualized rehabilitation. However, evaluation of naming errors is typically by subjective and laborious dichotomous classification. As a result, these evaluations do not capture the degree of semantic similarity and are susceptible to lower inter-rater reliability because of subjectivity. We investigated whether a computational linguistic measure, word2vec (Mikolov, Chen, Corrado, & Dean, 2013) addressed these limitations by evaluating errors during object naming in a group of patients during the acute stage of a left-hemisphere stroke (N=105). Pearson correlations demonstrated excellent convergent validity of word2vec’s semantically related estimates of naming errors and independent tests of access to lexical-semantic knowledge (p’s < .0001). Further, multiple regression analysis showed word2vec’s semantically related estimates were significantly better than human error classification at predicting performance on tests of lexical-semantic knowledge (p < .001). Useful to both theorists and clinicians, word2vec provides an automated, continuous, and objective psychometric measure of access to lexical-semantic knowledge during naming.

Ten Years of BabelNet: A Survey

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI. However, when it comes to Natural Language Processing (NLP), symbols have to be mapped to words and phrases, which are not only ambiguous but also language-specific: multilinguality is indeed a desirable property for NLP systems, and one which enables the generalization of tasks where multiple languages need to be dealt with, without translating text. In this paper we survey BabelNet, a popular wide-coverage lexical-semantic knowledge resource obtained by merging heterogeneous sources into a unified semantic network that helps to scale tasks and applications to hundreds of languages. Over its ten years of existence, thanks to its promise to interconnect languages and resources in structured form, BabelNet has been employed in countless ways and directions. We first introduce the BabelNet model, its components and statistics, and then overview its successful use in a wide range of tasks in NLP as well as in other fields of AI.

Lexical Semantic Knowledge of Children with ASD—a Review Study

Studies on the lexical semantic abilities of children with autism have yielded contradicting results. The aim of the current review was to explore studies that have specifically focused on the lexical semantic abilities of children with ASD and try to find an explanation for these contradictions. In the 32 studies reviewed, no single factor was found to affect lexical semantic skills, although children with broader linguistic impairment generally, but not universally, also showed impaired lexical semantic skills. The need for future studies with young ASD participants, with differing intellectual functioning, longitudinal studies, and studies assessing a wide range of language domains are discussed.

Exploring the relationship between language and Europe from a cognitive-linguistic perspective using Concept Maps

This paper aims to explore the mentally represented and linguistically bound concepts of Europe of young learners. Special attention is not given to the declarative and school generated knowledge about the EU, but rather to the lexical-semantic knowledge of the learners, which is activated at the lexical impulse {euro[pa/ä]} as part of a concept in the mental lexicon. It aims to find out which knowledge elements learners link with the lexical impulse and which dispositions and pre-concepts the learners have when it comes to Europe in the school context, in educational media or in everyday teaching-learning-situations. Therefore, concept maps are used as a survey instrument and evaluated using a corpus-linguistic approach.

Settling Into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

Most words are ambiguous: Individual word forms (e.g., run) can map onto multiple different interpretations depending on their sentence context (e.g., the athlete/politician/river runs). Models of word-meaning access must therefore explain how listeners and readers can rapidly settle on a single, contextually appropriate meaning for each word that they encounter. I present a new account of word-meaning access that places semantic disambiguation at its core and integrates evidence from a wide variety of experimental approaches to explain this key aspect of language comprehension. The model has three key characteristics. (a) Lexical-semantic knowledge is viewed as a high-dimensional space; familiar word meanings correspond to stable states within this lexical-semantic space. (b) Multiple linguistic and paralinguistic cues can influence the settling process by which the system resolves on one of these familiar meanings. (c) Learning mechanisms play a vital role in facilitating rapid word-meaning access by shaping and maintaining high-quality lexical-semantic knowledge throughout the life span. In contrast to earlier models of word-meaning access, I highlight individual differences in lexical-semantic knowledge: Each person’s lexicon is uniquely structured by specific, idiosyncratic linguistic experiences.

Growth in narrative retelling and inference abilities and relations with reading comprehension in children and adolescents with autism spectrum disorder

Background and aims Extant research indicates that children and adolescents with autism spectrum disorder (ASD) without an intellectual disability (ID) often experience difficulty comprehending written texts that is unexpected in comparison with their cognitive abilities. This study investigated the development of two key skills, narrative and inference abilities, that support higher level text comprehension and their relation to lexical-semantic knowledge, ASD symptomatology, and age. Three questions were addressed: 1.) What was the nature of narrative and inference skill development over time? 2.) What was the relation between narrative or inference development and lexical-semantic knowledge, ASD symptomatology, and age? 3.) Did initial narrative and inferencing skills, and the development of these skills, predict reading comprehension outcomes? Methods: Data from 81 children and adolescents with ASD without ID (FIQ ≥ 75) between the ages of 8-16-years-old at timepoint 1 were collected at 15-month intervals across three timepoints. ASD symptomatology was assessed with the ADOS-2. Standardized narrative retelling, inference, reading comprehension, lexical-semantic knowledge and cognitive assessments were administered. Latent growth curve models were conducted to examine narrative and inference skill development, and conditional growth models were fit to examine the relation between growth trajectories and covariates (lexical-semantic knowledge, ASD symptomatology, age) as well as with the reading comprehension distal outcome. Results Narrative retelling skills followed a linear trajectory of growth and were a relative strength in this sample, while inference skills were well below average and declined over time relative to age-normed standard scores. Lexical-semantic knowledge explained significant heterogeneity in initial narrative and inference skills, whereas ASD symptomatology was only related to initial narrative retelling abilities and age was only related to initial inference abilities. Timepoint 3 reading comprehension skill (in the below average range) was significantly explained by initial narrative retelling and inference abilities. Conclusions The results of this study indicate that narrative retelling and inference skills are important for successful reading comprehension for individuals with ASD without ID and that lexical-semantic knowledge underpins these skills. Furthermore, the observation that ASD symptom severity was associated with narrative retelling skills is consistent with the hypothesis that problems in narrative reading skills are associated with the autism phenotype. Finally, inference skill was a particular challenge for individuals in this sample, although age was positively associated with better performance on the assessment. Implications: These findings suggest that narrative and inference skills, in addition to lexical-semantic knowledge, are important to target beginning in elementary grades to improve reading comprehension outcomes for children and adolescents with ASD without ID.

Semantic Category Convergence in Spanish–English Bilingual Children With and Without Developmental Language Disorder

Purpose The study examines the extent of convergence of semantic category members in Spanish–English bilingual children with reference to adults using a semantic fluency task. Method Thirty-seven children with developmental language disorder (DLD), matched pairwise with 37 typically developing (TD) children in the age range of 7;0–9;11 (years;months), produced items in 7 semantic categories (3 taxonomic and 4 slot-filler) in both Spanish and English. The 10 most frequently produced items for each category by 20 Spanish–English bilingual adults were identified as the most prototypical responses. The top 10 items generated by TD children and children with DLD, in their order of production, were analyzed for the amount of convergence with adults' responses. Results The top 5 items produced by children with DLD showed similar convergence scores as those produced by their TD peers. However, their responses in the 6th to 10th positions showed lower convergence scores than their TD peers. Children's convergence scores were higher for the slot-filler condition compared to taxonomic in both English and Spanish. The convergence scores also significantly differed across the semantic categories. Conclusion The children with DLD show greater convergence on the typical items generated earlier in their word lists than the items generated later. This pattern of convergence and divergence highlights their strengths and weaknesses in the representation of lexical–semantic knowledge for typical versus less typical items. Supplemental Material https://doi.org/10.23641/asha.8323613

Settling into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

Most words are ambiguous: individual wordforms (e.g., “run”) can map onto multiple different interpretations depending on their sentence context (e.g., “the athlete/politician/river runs”). Models of word-meaning access must therefore explain how listeners and readers are able to rapidly settle on a single, contextually appropriate meaning for each word that they encounter. This article presents a new account of word meaning that places semantic disambiguation at its core, and integrates evidence from a wide variety of experimental approaches to explain this key aspect of language comprehension. The model has three key characteristics. (i) Lexical-semantic knowledge is viewed as a high-dimensional space; familiar word meanings correspond to stable states within this lexical-semantic space. (ii) Multiple linguistic and paralinguistic cues can influence the settling process by which the system resolves on one of these familiar meanings. (iii) Learning mechanisms play a vital role in facilitating rapid word-meaning access by shaping and maintaining high quality lexical-semantic knowledge. Several key areas for future research are identified.