The Relationship of Lexical Richness to the Quality of ESL Learners' Oral Narratives.

By Xiaofei Lu. 2012

606 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Language and Linguistics, Natural Language Processing

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Citations in the corpus

Creating Training Corpora for NLG Micro-Planners (2017, neutral): “We compute LS and CTTR using the Lexical Complexity Analyzer developed by Lu (2012). sophisticated (higher proportion of unfrequent words) than RNNLG’s.”
The E2E Dataset: New Challenges For End-to-End Generation (2017, methodology): “We complement the traditional measure of lexical diversity type-token ratio (TTR) with the more robust measure of mean segmental TTR (MSTTR) (Lu, 2012), which divides the corpus into successive segments of a given length and then calculates the average TTR of all segments.”
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge (2019, methodology): “In order to measure various dimensions of lexical richness in the datasets under comparison, we computed statistics on token/unigram, bigram and trigram counts, and we applied the Lexical Complexity Analyser (Lu, 2012), as shown in Table 4.”
Analysing Data-To-Text Generation Benchmarks (2017, methodology): “the three data-sets support the training of lexically rich surface realisers, we used the Lexical Complexity Analyser developed by (Lu, 2012), a system designed to automate the measurement of various dimensions of lexical richness.”
Second language speech production: Investigating linguistic correlates of comprehensibility and accentedness for learners at different ability levels (2016, background): “Thus, longer speech samples may be needed (cf. 3 min in Lu, 2012; and 5 min in Foster & Skehan, 1996; and Yuan & Ellis, 2003) in order to obtain a more refined picture of lexical, grammatical, and temporal correlates of comprehensibility and accentedness.”
Survey on evaluation methods for dialogue systems (2019, background): “For a more in-depth discussion please refer to Lu (2012).”
SLURP: A Spoken Language Understanding Resource Package (2020, methodology): “In addition, we compute the following lexicographic measures using the Lexical Complexity Analyser (Lu, 2012).”
Lexical Sophistication as a Multidimensional Phenomenon: Relations to Second Language Lexical Proficiency, Development, and Writing Quality (2018, positive): “the remaining variance in human judgments of lexical proficiency may be related to other elements important for predicting lexical proficiency such as fluency, comprehensibility, lexical diversity, proportion of lexical errors, and L2 learners’ metacognitive strategies for lexical use (Chapelle, 199”
Fact-Checking: A Meta-Analysis of What Works and for Whom (2019, background): “…word variation (M = .71, SD = .13), verb variation (M = .18, SD = .12), and noun variation (M = .66, SD = .13) (for more information see Ai & Lu, 2010; Lu, 2012).8 Then, the four different indicators were averaged to create a single composite of lexical complexity (M = .47, SD = .09, α = .82).”
Exploring the relationship between productive vocabulary knowledge and second language oral ability (2019, methodology): “further investigate the relationship between vocabulary knowledge and L2 speaking ability, it is important to be aware of other aspects of vocabulary knowledge besides frequency such as L1 cognate (Bardel, Gudmundson, & Lindqvist, 2012), speed of lexical retrieval (Fitzpatrick & Izura, 2011; Miralpe”
The Development of Second Language Writing Complexity in Groups and Individuals: A Longitudinal Learner Corpus Study (2012, background): “CTTR is calculated in word types divided by the square root of 2 times the total number of words and thus “takes the length of the sample into account to avoid the problem that regular type–token ratios are affected by length” (Larsen‐Freeman, 2006, p. 597, see also Lu, 2012).”
An update on discourse functions and syntactic complexity in synchronous and asynchronous communication (2016, positive): “…of learners, and between syntactic complexity and the development of conversational and pragmatic competence in computer-mediated environments (e.g., Kreyer & Schaub, 2015; Lazarte & Barry, 2008; Lu, 2011, 2012; Mazgutova & Kormos, 2015; Parkinson & Musgrave, 2014; Sherratt, 2007; Vyatkina, 2012).”