Studies in language behavior: A program of research

By W. Johnson. 1944

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Citations in the corpus

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2022, background): “Specifically, we provide interfaces to 1) n-gram overlap metrics metrics such as ROUGE (Lin, 2004), BLEU (Papineni et al., 2002), SacreBLEU (Post, 2018), METEOR (Banerjee & Lavie, 2005); (2) model-based semantic metrics such as BertScore (Zhang et al., 2019) and BLEURT (Sellam et al., 2020) which ge”
MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment (2010, methodology): “To better understand the rationale of the factors that form the MTLD value, we must first consider the LD index known as mean segmental TTR (MSTTR; Johnson, 1944).”
Investigating accommodation in language proficiency interviews using a new measure of lexical diversity (2002, methodology): “In their study of oral interviews, the authors (Richards and Malvern, 2000) addressed the problem of calculating lexical diversity from varying sample sizes by using the Mean Segmental Type–Token Ratio (MSTTR), an index that appears to have been originally recommended by Johnson (1944).”
SLURP: A Spoken Language Understanding Resource Package (2020, background): “The Mean Segmental Text-to-Token Ratio (MSTTR) (Johnson, 1944) is the average Text-to-Token Ratio (TTR –”
Cutting the Gordian Knot: The Moving-Average Type–Token Ratio (MATTR) (2010, methodology): “MATTR is more informative than the mean segment TTR (MSTTR) introduced by Johnson (1944), advocated by Schach (1987), cited by Ko¨hler and Galle (1993), discussed by Malvern and Richards (2001), and implemented in WordSmith 4.0 (Oxford University Press).”
Assessing the Validity of Lexical Diversity Indices Using Direct Judgements (2020, background): “, total number of words) in a text (Johnson, 1944).”
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment (2023, methodology): “The mean reward evaluated on the hand-out dataset and the perplexity are the main criteria for us to evaluate models and we also take the diversity metrics (Table 3) (Ramamurthy et al., 2022) into consideration, including Mean Segmented Type Token Ratio (MSSTR) (Johnson, 1944), the Distinct-1, Disti”
Back to Basics: How Measures of Lexical Diversity Can Help Discriminate between CEFR Levels (2016, background): “This measure is often attributed to Templin (1957) but is already mentioned by (Johnson 1944).”
The Relationship of Lexical Richness to the Quality of ESL Learners' Oral Narratives. (2012, background): “For the NDW-based measures and MSTTR, we used 50 as the standard number, as the shortest sample in our dataset was 105 words long.”
On the role of morphological richness in the early development of noun and verb inflection (2011, methodology): “However, Xanthos and Gillis (2010) show that this dependence can be controlled for by applying a resampling procedure based on the work of Johnson (1944).”
The relationship between vocabulary and writing quality in three genres (2012, background): “Earlier measures simply included the number of different or unique words (Barenbaum, Newcomer, & Nodine, 1987; Grobe, 1981) or a ratio of types to tokens (i.e., number of words written; Johnson, 1944).”