Measuring Lexical Style and Competence: The Type-Token Vocabulary Curve

By G. Youmans. 1990

Subfields: Natural Language Processing, Computational Linguistics & Semantics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Xin Eric Wang
2. Lei Li
3. William Yang Wang
4. Piek T. J. M. Vossen
5. Yuan-fang Wang
6. Emiel van Miltenburg
7. Jiawei Wu
8. Junkun Chen

In Computational Linguistics & Semantics

1. Lei Li
2. William Yang Wang
3. Piek T. J. M. Vossen
4. Yuan-fang Wang
5. Emiel van Miltenburg
6. Xin Eric Wang
7. Jiawei Wu
8. Junkun Chen

Citations in the corpus

• VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research (2019, methodology): “For a more intuitive measure of the lexical richness and caption diversity, we then propose the Type-Caption Curve , which is adapted from the type-token vocabulary curve [64] but specially designed for the caption corpora here.”
• Measuring the Diversity of Automatic Image Descriptions (2018, methodology): “We visualize the differences between the systems using a type-token curve (TTC) , which shows how the number of types develops as one reads more output tokens (Youmans, 1990).”