Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application

By K. Kyle, Scott Andrew Crossley. 2015

484 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/50a1c1283fb9b263632babdb36d2f8d01a31f7be

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In Developmental and Educational Psychology

Citations in the corpus

Exploring the relationship between productive vocabulary knowledge and second language oral ability (2019, background): “…and Lindqvist 2012), speed of lexical retrieval (Fitzpatrick and Izura 2011; Miralpeix and Meara 2014), abstractness (Crossley, Salsbury and McNamara 2009), sense relations (Crossley, Salsbury and McNamara 2010), richness and sophistication (Lu 2012), and multi-word units (Kyle and Crossley 2014).”
Lexical Sophistication as a Multidimensional Phenomenon: Relations to Second Language Lexical Proficiency, Development, and Writing Quality (2018, unknown): “…models also indicated that more proficient L2 learners tend to use a higher proportion of ngrams found in the reference corpora n-gram lists (Kyle & Crossley, 2015), that the more strongly associated bigrams a L2 learner uses, themore likely the learner is to be judged as being more…”
Assessing syntactic sophistication in L2 writing: A usage-based approach (2017, background): “…as seen in more recent assessment research that has reported strong links between scores of writing quality and the degree of test takers’ use of certain multi-word units identified through corpus-based research (Bestgen & Granger, 2014; Crossley, Cai, & McNamara, 2012; Kyle & Crossley, 2015).”
Linguistic features in writing quality and development: An overview (2020, background): “Kyle and Crossley (2015) also reported that more proficient L2 writers produced more frequent trigrams as found in the written portion of the BNC than lower proficiency writers.”
Task-Based Learner Production: A Substantive and Methodological Review (2016, result): “This approach would not only allow for others to reexamine an original set of linguistic analyses, but also to replicate the analyses and generalize the results to other learners, tasks, and so forth (Kyle & Crossley, 2015).”
Applications of deep learning in stock market prediction: recent progress (2020, unknown): “(2019) eLSTM SVM, DT, ANN, LSTM, AZFin Text (Schumaker & Chen, 2009), TeSIA (Li et al., 2016a) Tran et al. (2018) TABL Ridge, FFNN, LDA, MDA, MTR, WMTR (Tran et al.”