Predicting Lexical Richness Using Computational Indices in Argumentative Writing of Graduate Iranian TEFL Learners

The researchers present a model of lexical proficiency based on lexical indices related to vocabulary size, depth of lexical knowledge, and accessibility to core lexical items. The lexical indices used in this study come from the computational tool Coh-Metrix and include word length scores, lexical diversity values, word frequency counts, hypernymy values, polysemy values, semantic co-referentiality, word meaningfulness, word concreteness, word imagability, and word familiarity. Human raters evaluated a corpus of ۲۴۰ written texts using a standardized rubric of lexical proficiency. To ensure a variety of text levels, the corpus comprised ۶۰ texts each from beginning, intermediate, and advanced second language (L۲) adult English learners. The L۲ texts were collected longitudinally from ۱۰ English learners. The holistic scores from the trained human raters were then correlated to a variety of lexical indices. The researchers found that lexical diversity, word hypernymy values and content word frequency explain ۴۴% of the variance of the human evaluations of lexical proficiency in the examined writing samples. The findings represent an important step in the development of a model of lexical proficiency that incorporates both vocabulary size and depth of lexical knowledge features.

Keywords:

“computational linguistics” , “depth of lexical knowledge” , “lexical diversity” , “lexical frequency”