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1. Relating referential clarity and phonetic clarity in infant‐directed speech

   https://doi.org/10.1111/desc.13442  

   Beech, Caroline ; Swingley, Daniel ( August 2023 , Developmental Science)

   Psycholinguistic research on children's early language environments has revealed many potential challenges for language acquisition. One is that in many cases, referents of linguistic expressions are hard to identify without prior knowledge of the language. Likewise, the speech signal itself varies substantially in clarity, with some productions being very clear, and others being phonetically reduced, even to the point of uninterpretability. In this study, we sought to better characterize the language‐learning environment of American English‐learning toddlers by testing how well phonetic clarity and referential clarity align in infant‐directed speech. Using an existing Human Simulation Paradigm (HSP) corpus with referential transparency measurements and adding new measures of phonetic clarity, we found that the phonetic clarity of words’ first mentions significantly predicted referential clarity (how easy it was to guess the intended referent from visual information alone) at that moment. Thus, when parents’ speech was especially clear, the referential semantics were also clearer. This suggests that young children could use the phonetics of speech to identify globally valuable instances that support better referential hypotheses, by homing in on clearer instances and filtering out less‐clear ones. Such multimodal “gems” offer special opportunities for early word learning.

   In parent‐infant interaction, parents’ referential intentions are sometimes clear and sometimes unclear; likewise, parents’ pronunciation is sometimes clear and sometimes quite difficult to understand.

   We find that clearer referential instances go along with clearer phonetic instances, more so than expected by chance.

   Thus, there are globally valuable instances (“gems”) from which children could learn about words’ pronunciations and words’ meanings at the same time.

   Homing in on clear phonetic instances and filtering out less‐clear ones would help children identify these multimodal “gems” during word learning.

    

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2. KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker

   Brody Downs, Oghenemaro Anuyah ( January 2020 , Proceedings of the 12th Language Resources and Evaluation Conference (LREC))

   For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model’s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community. 

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3. Neurocognitive mechanisms of co‐occurring math difficulties in dyslexia: Differences in executive function and visuospatial processing

   https://doi.org/10.1111/desc.13443  

   Marks, Rebecca A. ; Pollack, Courtney ; Meisler, Steven L. ; D'Mello, Anila M. ; Centanni, Tracy M. ; Romeo, Rachel R. ; Wade, Karolina ; Matejko, Anna A. ; Ansari, Daniel ; Gabrieli, John D. E. ; et al ( September 2023 , Developmental Science)

   Children with dyslexia frequently also struggle with math. However, studies of reading disability (RD) rarely assess math skill, and the neurocognitive mechanisms underlying co‐occurring reading and math disability (RD+MD) are not clear. The current study aimed to identify behavioral and neurocognitive factors associated with co‐occurring MD among 86 children with RD. Within this sample, 43% had co‐occurring RD+MD and 22% demonstrated a possible vulnerability in math, while 35% had no math difficulties (RD‐Only). We investigated whether RD‐Only and RD+MD students differed behaviorally in their phonological awareness, reading skills, or executive functions, as well as in the brain mechanisms underlying word reading and visuospatial working memory using functional magnetic resonance imaging (fMRI). The RD+MD group did not differ from RD‐Only on behavioral or brain measures of phonological awareness related to speech or print. However, the RD+MD group demonstrated significantly worse working memory and processing speed performance than the RD‐Only group. The RD+MD group also exhibited reduced brain activations for visuospatial working memory relative to RD‐Only. Exploratory brain‐behavior correlations along a broad spectrum of math ability revealed that stronger math skills were associated with greater activation in bilateral visual cortex. These converging neuro‐behavioral findings suggest that poor executive functions in general, including differences in visuospatial working memory, are specifically associated with co‐occurring MD in the context of RD.

   Children with reading disabilities (RD) frequently have a co‐occurring math disability (MD), but the mechanisms behind this high comorbidity are not well understood.

   We examined differences in phonological awareness, reading skills, and executive function between children with RD only versus co‐occurring RD+MD using behavioral and fMRI measures.

   Children with RD only versus RD+MD did not differ in their phonological processing, either behaviorally or in the brain.

   RD+MD was associated with additional behavioral difficulties in working memory, and reduced visual cortex activation during a visuospatial working memory task.

    

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4. A Multi-match Approach to the Author Uncertainty Problem

   https://doi.org/10.2478/jdis-2019-0006  

   Carley, Stephen F. ; Porter, Alan L. ; Youtie, Jan L. ( June 2019 , Journal of Data and Information Science)

   Abstract Purpose The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies. Design/methodology/approach The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science (WOS) search for a given author’s last name, followed by a comma, followed by the first initial of his or her first name (e.g., a search for ‘John Doe’ would assume the form: ‘Doe, J’). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database (i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author (i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J’ and ‘Doe, John’ share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem. Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John’ and ‘Doe, J’ have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it’s conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they’re too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination. Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint (see www.thevantagepoint.com) While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user’s part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets. Our script begins by prompting the user for a surname and a first initial (for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name (referred to by the script as the primary author) within this field whom the user knows to be a true positive (a suggested approach is to point to an author name associated with one of the records that has the author’s ORCID iD or email address attached to it). The script proceeds to identify and combine all author names sharing the primary author’s surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller (and more manageable) dataset to manually inspect (and/or apply additional name disambiguation techniques to). Research limitations Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user’s part. Our procedure doesn’t lend itself to scholars who have had a legal family name change (after marriage, for example). Moreover, the technique we advance is (sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary. Practical implications The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist. Originality/value Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both. Findings Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications. While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest (e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives (at least for more common surnames), it does significantly reduce manual effort on the user’s part. Dataset reduction (after our procedure is applied) is in large part a function of (a) field availability and (b) field coverage. 

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5. Searching for spellcheckers: What kids want, what kids need

   https://doi.org/10.1145/3311927.3325328  

   Downs, Brody ; French, Tyler ; Pera, Maria Soledad ; Wright, Katherine Landau ; Kennington, Casey ; Fails, Jerry Alan ( June 2019 , Proceedings of the 18th ACM International Conference on Interaction Design and Children)

   Misspellings in queries used to initiate online searches is an everyday occurrence. When this happens, users either rely on the search engine's ability to understand their query or they turn to spellcheckers. Spellcheckers are usually based on popular dictionaries or past query logs, leading to spelling suggestions that often better resonate with adult users because that data is more readily available. Based on an educational perspective, previous research reports, and initial analyses of sample search logs, we hypothesize that existing spellcheckers are not suitable for young users who frequently encounter spelling challenges when searching for information online. We present early results of our ongoing research focused on identifying the needs and expectations children have regarding spellcheckers. 

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